diff --git a/config_juxiting.py b/config_juxiting.py
index 20003ff..79c29d1 100644
--- a/config_juxiting.py
+++ b/config_juxiting.py
@@ -211,7 +211,7 @@ upload_data = {
 ### 开关
 is_train = True # 是否训练
 is_debug = False # 是否调试
-is_eta = True # 是否使用eta接口
+is_eta = False # 是否使用eta接口
 is_timefurture = True # 是否使用时间特征
 is_fivemodels = False # 是否使用之前保存的最佳的5个模型
 is_edbcode = False # 特征使用edbcoding列表中的
diff --git a/juxitingdataset/PP指标数据.xlsx b/juxitingdataset/PP指标数据.xlsx
index ed09d60..fac7701 100644
Binary files a/juxitingdataset/PP指标数据.xlsx and b/juxitingdataset/PP指标数据.xlsx differ
diff --git a/juxitingdataset/df_combined3.csv b/juxitingdataset/df_combined3.csv
index 9977fc0..7278ce7 100644
--- a/juxitingdataset/df_combined3.csv
+++ b/juxitingdataset/df_combined3.csv
@@ -1,206 +1,206 @@
-ds,NHITS,Informer,LSTM,iTransformer,TSMixer,TSMixerx,PatchTST,RNN,GRU,TCN,BiTCN,DilatedRNN,MLP,DLinear,NLinear,TFT,StemGNN,MLPMultivariate,TiDE,DeepNPTS,y,min_within_quantile,max_within_quantile,min_model,max_model,NLinear_abs_error_rate,RNN_abs_error_rate,TiDE_abs_error_rate,TSMixer_abs_error_rate,PatchTST_abs_error_rate,NHITS_abs_error_rate,DilatedRNN_abs_error_rate,DLinear_abs_error_rate,GRU_abs_error_rate,LSTM_abs_error_rate,Informer_abs_error_rate,DeepNPTS_abs_error_rate,MLP_abs_error_rate,TFT_abs_error_rate,TSMixerx_abs_error_rate,BiTCN_abs_error_rate,TCN_abs_error_rate,iTransformer_abs_error_rate,StemGNN_abs_error_rate,MLPMultivariate_abs_error_rate,min_abs_error_rate_prediction,min_abs_error_rate_column_name
-2024-01-30,7314.25,7335.76,7255.32,7375.61,7296.98,7304.24,7288.53,7316.8,7357.77,7406.36,7330.32,7284.31,7336.16,7282.57,7305.31,7289.84,7306.12,7295.54,7297.12,7304.88,7315.0,7288.53,7316.8,PatchTST,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,7314.25,NHITS
-2024-01-31,7313.85,7333.85,7260.27,7376.32,7306.93,7303.91,7291.97,7327.11,7364.25,7409.82,7340.18,7298.74,7321.85,7294.2,7298.47,7302.1,7304.55,7299.61,7302.49,7313.87,7302.5,7291.97,7327.11,PatchTST,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,7302.49,TiDE
-2024-02-01,7297.0,7335.21,7250.98,7385.46,7298.8,7304.56,7284.13,7314.06,7351.66,7406.99,7334.74,7290.45,7319.52,7291.02,7287.84,7290.86,7303.42,7300.2,7301.71,7297.39,7275.0,7284.13,7314.06,PatchTST,RNN,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.02,0.02,0.0,0.0,7284.13,PatchTST
-2024-02-02,7275.51,7314.77,7233.06,7349.83,7272.41,7282.94,7263.86,7294.73,7330.68,7395.3,7306.72,7260.65,7289.64,7278.71,7262.57,7266.32,7300.92,7272.72,7280.73,7271.37,7242.5,7262.57,7294.73,NLinear,RNN,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.02,0.01,0.01,0.0,7233.06,LSTM
-2024-02-04,7236.79,7273.36,7212.67,7264.81,7239.09,7232.82,7228.47,7267.82,7308.6,7378.92,7271.96,7228.76,7248.22,7249.75,7245.09,7227.47,7297.04,7210.26,7249.74,7287.39,7240.0,7228.47,7267.82,PatchTST,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.02,0.0,0.01,0.0,7239.09,TSMixer
-2024-02-05,7237.9,7254.36,7211.69,7215.08,7225.67,7197.03,7226.16,7266.29,7310.45,7374.66,7241.34,7223.16,7238.12,7228.83,7231.89,7224.25,7292.84,7185.72,7230.67,7307.89,7227.5,7225.67,7266.29,TSMixer,RNN,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.02,0.0,0.01,0.01,7228.83,DLinear
-2024-02-06,7224.19,7245.77,7203.25,7200.75,7215.03,7174.85,7206.81,7257.77,7300.49,7369.7,7214.12,7210.87,7217.43,7212.0,7214.21,7214.99,7288.06,7159.48,7215.9,7300.92,7246.67,7206.81,7257.77,PatchTST,RNN,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.02,0.01,0.01,0.01,7245.77,Informer
-2024-02-07,7237.08,7241.63,7216.83,7221.2,7226.42,7222.58,7234.44,7277.5,7317.63,7377.89,7198.22,7229.34,7228.86,7227.31,7229.82,7221.54,7284.15,7172.75,7230.4,7282.88,7255.0,7226.42,7277.5,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.02,0.0,0.0,0.01,7241.63,Informer
-2024-02-08,7249.03,7225.22,7221.61,7236.65,7242.4,7235.89,7252.49,7285.7,7321.4,7382.84,7189.21,7239.06,7239.93,7231.63,7251.7,7236.11,7280.63,7193.89,7252.08,7254.66,7270.0,7242.4,7285.7,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.02,0.0,0.0,0.01,7280.63,StemGNN
-2024-02-18,7257.58,7232.66,7232.76,7247.51,7258.08,7252.65,7269.86,7295.25,7334.03,7388.26,7192.33,7253.86,7251.03,7230.31,7268.39,7263.02,7278.08,7215.18,7272.44,7244.53,7336.67,7258.08,7295.25,TSMixer,RNN,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.01,0.01,0.01,0.02,7334.03,GRU
-2024-02-19,7329.8,7284.9,7279.87,7271.41,7312.25,7328.12,7333.28,7359.55,7391.84,7412.85,7285.85,7321.05,7295.19,7280.82,7344.06,7349.32,7277.16,7268.04,7323.31,7273.12,7350.0,7312.25,7359.55,TSMixer,RNN,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.0,0.01,0.01,0.01,0.01,0.01,7349.32,TFT
-2024-02-20,7343.01,7301.36,7287.02,7283.48,7338.11,7398.41,7344.57,7358.8,7393.28,7424.57,7353.08,7341.83,7322.19,7304.78,7354.74,7371.01,7276.7,7309.68,7334.03,7338.48,7335.0,7334.03,7358.8,TiDE,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.0,7334.03,TiDE
-2024-02-21,7331.02,7289.8,7277.52,7284.16,7334.13,7417.16,7333.0,7344.13,7379.12,7429.64,7381.22,7344.5,7320.66,7332.96,7337.17,7360.45,7275.86,7320.94,7325.44,7337.84,7297.5,7325.44,7344.13,TiDE,RNN,0.01,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.02,0.01,0.02,0.0,0.0,0.0,7289.8,Informer
-2024-02-22,7286.55,7266.5,7249.85,7285.34,7304.14,7392.3,7305.8,7314.63,7345.37,7420.07,7373.2,7311.53,7302.57,7318.15,7298.16,7284.19,7273.93,7304.0,7292.32,7342.6,7285.0,7292.32,7314.63,TiDE,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.02,0.0,0.0,0.0,7285.34,iTransformer
-2024-02-23,7272.61,7258.89,7241.55,7287.63,7288.24,7365.99,7296.96,7295.77,7340.09,7413.29,7359.23,7296.67,7292.49,7303.51,7281.81,7269.99,7271.49,7289.99,7281.0,7323.98,7306.67,7281.0,7296.96,TiDE,PatchTST,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.0,0.0,7303.51,DLinear
-2024-02-26,7318.19,7258.93,7254.44,7295.47,7299.03,7362.35,7321.52,7317.68,7358.98,7413.28,7357.37,7301.95,7320.65,7296.1,7301.41,7307.13,7269.8,7306.61,7295.1,7371.11,7292.5,7295.1,7321.52,TiDE,PatchTST,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.0,0.01,0.01,0.02,0.0,0.0,0.0,7295.1,TiDE
-2024-02-27,7324.57,7251.97,7242.58,7290.08,7297.98,7317.99,7297.46,7304.28,7342.81,7408.67,7340.79,7289.78,7327.02,7292.81,7282.36,7282.91,7268.05,7299.63,7286.47,7368.65,7306.67,7282.36,7304.28,NLinear,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,7304.28,RNN
-2024-02-28,7352.75,7295.49,7254.83,7276.21,7302.82,7348.5,7323.02,7324.8,7359.94,7413.98,7330.58,7305.92,7341.13,7310.9,7305.61,7301.83,7266.96,7303.46,7303.91,7380.02,7353.33,7302.82,7324.8,TSMixer,RNN,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.01,7352.75,NHITS
-2024-02-29,7365.17,7313.22,7288.96,7293.72,7334.31,7378.47,7368.02,7371.75,7401.12,7425.51,7341.83,7336.51,7371.22,7333.33,7360.72,7354.87,7266.78,7327.83,7343.33,7374.52,7325.0,7334.31,7371.75,TSMixer,RNN,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.0,7327.83,MLPMultivariate
-2024-03-01,7320.5,7306.35,7263.73,7277.35,7322.0,7343.2,7329.76,7327.44,7364.73,7416.31,7322.68,7306.52,7337.22,7315.45,7335.42,7314.74,7266.41,7308.63,7327.13,7294.14,7343.33,7322.0,7335.42,TSMixer,NLinear,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7343.2,TSMixerx
-2024-03-04,7334.56,7330.43,7283.3,7276.89,7330.63,7344.31,7334.27,7357.22,7391.92,7422.3,7315.67,7328.23,7328.13,7333.05,7339.2,7314.98,7266.39,7306.47,7342.18,7296.81,7330.0,7330.63,7357.22,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7330.43,Informer
-2024-03-05,7309.45,7325.04,7267.88,7277.67,7326.97,7335.79,7319.16,7340.15,7370.5,7418.13,7305.37,7318.76,7320.53,7326.05,7321.28,7293.51,7266.15,7303.62,7328.04,7281.27,7310.0,7319.16,7340.15,PatchTST,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,7309.45,NHITS
-2024-03-06,7304.78,7318.4,7255.75,7276.86,7305.71,7311.24,7313.83,7311.53,7357.06,7412.45,7301.94,7304.3,7315.76,7309.79,7297.15,7279.6,7265.64,7295.52,7301.35,7288.95,7297.5,7297.15,7313.83,NLinear,PatchTST,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.0,7297.15,NLinear
-2024-03-07,7302.21,7321.25,7246.71,7271.87,7294.17,7309.87,7308.66,7314.28,7347.75,7407.47,7294.43,7290.02,7319.87,7285.87,7297.02,7274.12,7264.97,7290.85,7293.32,7295.53,7290.0,7293.32,7314.28,TiDE,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.0,7290.02,DilatedRNN
-2024-03-08,7292.95,7299.1,7241.47,7246.3,7288.79,7302.01,7299.54,7302.63,7343.0,7403.52,7279.52,7288.72,7290.22,7284.58,7288.43,7271.91,7264.57,7279.09,7290.29,7294.8,7295.0,7288.43,7302.63,NLinear,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,7294.8,DeepNPTS
-2024-03-11,7283.5,7308.73,7245.06,7233.56,7289.62,7272.81,7302.29,7308.69,7348.58,7402.53,7275.18,7290.97,7265.94,7297.8,7293.46,7274.37,7264.62,7276.64,7289.43,7294.81,7285.0,7289.43,7308.69,TiDE,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.0,0.0,7283.5,NHITS
-2024-03-12,7264.78,7291.06,7238.1,7239.95,7277.58,7269.54,7282.67,7303.06,7339.16,7395.51,7277.12,7280.3,7244.14,7296.69,7275.29,7269.44,7265.88,7271.18,7270.28,7285.48,7283.33,7270.28,7303.06,TiDE,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.01,0.0,0.0,7282.67,PatchTST
-2024-03-13,7256.37,7287.32,7238.44,7251.53,7272.1,7275.87,7280.45,7301.9,7340.35,7398.0,7269.77,7285.73,7238.05,7303.68,7278.15,7276.54,7267.4,7268.86,7272.17,7252.64,7285.0,7272.1,7301.9,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.0,0.0,0.0,7285.73,DilatedRNN
-2024-03-14,7269.4,7282.06,7239.51,7265.08,7275.51,7277.1,7283.45,7304.5,7341.46,7397.56,7280.69,7284.09,7251.88,7273.88,7288.42,7290.29,7270.84,7275.91,7281.04,7264.11,7277.5,7275.51,7304.5,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.0,7277.1,TSMixerx
-2024-03-15,7264.23,7275.59,7234.82,7272.71,7271.08,7262.54,7274.31,7295.47,7335.31,7395.98,7277.6,7279.69,7256.92,7252.44,7269.11,7283.47,7273.29,7276.28,7279.45,7273.04,7353.33,7269.11,7295.47,NLinear,RNN,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.02,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,7335.31,GRU
-2024-03-18,7339.36,7324.94,7292.13,7287.28,7320.05,7302.48,7366.6,7371.87,7407.54,7422.89,7304.87,7348.6,7314.84,7304.7,7353.23,7347.31,7276.14,7311.7,7330.94,7344.93,7387.5,7320.05,7371.87,TSMixer,RNN,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.02,0.01,7371.87,RNN
-2024-03-19,7388.08,7385.8,7320.49,7300.95,7361.99,7336.57,7386.98,7397.01,7427.54,7439.6,7311.0,7378.46,7361.53,7326.46,7392.8,7379.9,7277.91,7348.62,7363.5,7387.24,7435.0,7361.99,7397.01,TSMixer,RNN,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.02,0.01,0.01,0.01,0.01,0.01,0.02,0.0,0.02,0.02,0.01,7439.6,TCN
-2024-03-20,7446.17,7409.96,7390.64,7318.43,7404.71,7354.35,7446.15,7449.59,7481.41,7465.93,7324.92,7439.02,7414.86,7384.65,7440.51,7410.61,7279.73,7384.11,7415.17,7432.14,7460.0,7404.71,7449.59,TSMixer,RNN,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.02,0.0,0.02,0.02,0.01,7465.93,TCN
-2024-03-21,7471.48,7410.82,7418.15,7359.09,7436.82,7382.55,7467.97,7485.88,7497.73,7485.05,7342.78,7469.99,7437.01,7433.13,7466.88,7407.96,7282.79,7422.37,7449.82,7435.73,7422.5,7436.82,7485.88,TSMixer,RNN,0.01,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.02,0.0,7422.37,MLPMultivariate
-2024-03-22,7417.1,7386.94,7359.75,7407.99,7419.15,7377.38,7398.66,7419.29,7445.15,7473.64,7348.41,7429.75,7410.0,7426.43,7427.33,7392.96,7286.4,7414.18,7431.13,7377.32,7402.5,7398.66,7431.13,PatchTST,TiDE,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.02,0.0,7398.66,PatchTST
-2024-03-25,7404.65,7354.68,7337.62,7438.0,7391.76,7372.08,7396.19,7403.69,7432.8,7463.86,7342.24,7408.89,7382.35,7426.01,7402.0,7391.26,7288.26,7395.28,7401.38,7352.68,7372.5,7391.76,7403.69,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7372.08,TSMixerx
-2024-03-26,7367.22,7322.27,7297.47,7379.26,7362.82,7379.8,7348.91,7365.84,7399.45,7449.14,7347.17,7376.77,7355.65,7392.1,7355.2,7356.75,7289.35,7365.31,7355.82,7332.97,7365.0,7348.91,7365.84,PatchTST,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,7365.31,MLPMultivariate
-2024-03-27,7348.99,7314.72,7292.01,7324.37,7348.72,7361.01,7349.25,7354.78,7399.57,7441.3,7353.53,7370.99,7335.98,7369.87,7345.71,7338.27,7290.38,7340.31,7351.88,7327.51,7345.0,7345.71,7354.78,NLinear,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,7345.71,NLinear
-2024-03-28,7315.95,7301.15,7273.48,7295.8,7330.9,7328.15,7333.3,7346.44,7379.1,7428.09,7353.19,7339.56,7312.39,7352.16,7328.61,7330.9,7291.17,7320.32,7333.7,7319.06,7345.0,7328.61,7346.44,NLinear,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7346.44,RNN
-2024-03-29,7330.24,7305.84,7275.81,7295.1,7325.45,7312.22,7328.93,7348.29,7385.13,7421.9,7338.67,7331.57,7296.93,7335.55,7336.98,7327.3,7292.35,7309.14,7332.74,7339.16,7345.0,7325.45,7348.29,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7348.29,RNN
-2024-04-01,7338.24,7312.92,7275.35,7297.79,7326.11,7304.88,7330.88,7350.42,7384.03,7416.0,7329.56,7326.53,7302.67,7334.68,7344.62,7326.0,7293.29,7308.76,7335.23,7344.61,7360.0,7326.11,7350.42,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.01,7350.42,RNN
-2024-04-02,7346.74,7332.9,7289.21,7307.99,7336.79,7317.73,7346.57,7364.25,7401.18,7422.32,7325.94,7337.44,7312.67,7330.29,7355.13,7331.72,7294.23,7313.7,7342.82,7353.49,7380.0,7336.79,7364.25,TSMixer,RNN,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,7364.25,RNN
-2024-04-03,7362.03,7378.23,7308.14,7335.29,7353.97,7345.56,7371.53,7386.28,7419.33,7428.12,7335.65,7363.3,7336.41,7329.53,7376.12,7363.62,7295.82,7333.87,7357.16,7375.72,7397.5,7353.97,7386.28,TSMixer,RNN,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,7386.28,RNN
-2024-04-07,7380.46,7399.9,7329.28,7342.79,7377.67,7353.99,7410.47,7402.89,7435.25,7440.61,7349.37,7386.35,7366.33,7351.98,7390.83,7357.89,7298.55,7362.64,7388.62,7390.25,7437.5,7377.67,7410.47,TSMixer,PatchTST,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.02,0.01,7435.25,GRU
-2024-04-08,7447.65,7446.34,7385.22,7363.92,7413.36,7379.34,7464.31,7450.51,7478.47,7458.92,7355.57,7434.99,7413.54,7377.16,7434.57,7405.02,7302.9,7409.96,7431.65,7422.17,7490.0,7413.36,7464.31,TSMixer,PatchTST,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.02,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.0,0.02,0.02,0.01,7478.47,GRU
-2024-04-09,7508.91,7451.4,7454.91,7495.67,7458.67,7404.45,7504.04,7508.22,7535.45,7498.07,7362.18,7500.56,7456.07,7436.46,7490.4,7479.82,7307.45,7470.89,7473.05,7482.98,7507.5,7458.67,7508.22,TSMixer,RNN,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.01,0.0,0.01,0.02,0.0,0.0,0.03,0.0,7508.22,RNN
-2024-04-10,7525.01,7463.69,7471.0,7599.2,7480.64,7407.01,7494.6,7518.41,7543.95,7529.9,7380.28,7529.39,7474.85,7472.81,7508.65,7484.08,7312.89,7512.26,7487.67,7500.6,7497.5,7480.64,7518.41,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.02,0.0,0.01,0.02,0.0,7494.6,PatchTST
-2024-04-11,7504.87,7435.27,7455.34,7541.44,7475.08,7402.14,7456.31,7507.48,7525.09,7529.47,7398.46,7516.28,7430.02,7497.04,7500.72,7442.53,7317.92,7486.73,7485.56,7459.59,7497.5,7456.31,7507.48,PatchTST,RNN,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.02,0.0,7497.04,DLinear
-2024-04-12,7479.46,7435.88,7455.09,7449.45,7465.52,7413.95,7465.76,7506.2,7527.13,7527.37,7413.39,7518.23,7412.17,7521.26,7487.64,7408.6,7322.92,7468.34,7477.26,7416.74,7506.67,7465.52,7506.2,TSMixer,RNN,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.02,0.01,7506.2,RNN
-2024-04-15,7472.32,7395.35,7463.68,7434.46,7464.76,7388.03,7470.02,7506.19,7537.05,7531.6,7428.35,7524.9,7414.0,7503.56,7498.12,7387.92,7327.57,7459.92,7479.79,7412.79,7482.5,7464.76,7506.19,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.0,7479.79,TiDE
-2024-04-16,7445.28,7384.64,7424.47,7387.4,7448.83,7379.28,7438.86,7474.01,7498.57,7514.49,7421.47,7486.23,7387.0,7473.29,7463.03,7381.95,7330.04,7305.22,7465.68,7344.09,7495.0,7438.86,7474.01,PatchTST,RNN,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.01,0.02,0.01,0.02,0.02,0.01,0.0,0.01,0.02,0.03,7498.57,GRU
-2024-04-17,7446.2,7378.36,7448.03,7381.32,7454.08,7369.86,7440.14,7500.86,7526.87,7516.48,7420.73,7502.13,7377.72,7467.53,7480.13,7386.93,7332.3,7268.14,7470.71,7356.98,7497.5,7440.14,7500.86,PatchTST,RNN,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.02,0.02,0.02,0.01,0.02,0.01,0.0,0.02,0.02,0.03,7500.86,RNN
-2024-04-18,7457.83,7363.61,7444.84,7383.72,7460.04,7373.79,7419.24,7500.61,7521.25,7508.95,7418.34,7495.19,7396.12,7451.62,7494.75,7389.4,7335.15,7228.79,7474.29,7391.17,7497.5,7419.24,7500.61,PatchTST,RNN,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.01,0.02,0.01,0.01,0.01,0.02,0.01,0.0,0.02,0.02,0.04,7495.19,DilatedRNN
-2024-04-19,7469.48,7391.86,7446.51,7398.17,7463.59,7377.7,7399.12,7494.52,7524.0,7505.1,7416.04,7489.72,7404.43,7461.7,7489.8,7391.95,7339.46,7183.49,7469.89,7418.57,7527.5,7399.12,7494.52,PatchTST,RNN,0.01,0.0,0.01,0.01,0.02,0.01,0.01,0.01,0.0,0.01,0.02,0.01,0.02,0.02,0.02,0.01,0.0,0.02,0.02,0.05,7524.0,GRU
-2024-04-22,7496.98,7406.58,7488.83,7424.42,7488.17,7396.38,7437.13,7540.59,7567.34,7522.83,7419.43,7523.84,7447.05,7465.61,7533.4,7406.44,7344.58,7230.71,7495.75,7421.88,7527.5,7437.13,7540.59,PatchTST,RNN,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.01,0.02,0.01,0.01,0.02,0.02,0.01,0.0,0.01,0.02,0.04,7523.84,DilatedRNN
-2024-04-23,7500.82,7452.72,7481.53,7395.13,7502.22,7423.83,7450.05,7529.01,7554.42,7525.62,7422.18,7519.67,7474.19,7493.49,7518.56,7409.24,7349.08,7271.15,7508.02,7407.19,7510.0,7450.05,7529.01,PatchTST,RNN,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.02,0.02,0.03,7508.02,TiDE
-2024-04-24,7497.27,7500.77,7459.8,7455.1,7496.63,7431.4,7476.97,7509.26,7532.07,7519.69,7425.21,7500.56,7480.36,7501.79,7492.2,7399.97,7354.25,7295.49,7501.93,7410.32,7487.5,7476.97,7509.26,PatchTST,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.0,0.0,0.02,0.03,7492.2,NLinear
-2024-04-25,7494.2,7521.97,7429.77,7471.33,7478.92,7441.51,7488.24,7489.95,7505.15,7494.85,7433.35,7474.95,7478.76,7484.28,7477.99,7392.8,7358.79,7279.72,7481.18,7420.67,7487.5,7477.99,7489.95,NLinear,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.0,0.0,0.02,0.03,7488.24,PatchTST
-2024-04-26,7487.55,7493.94,7433.02,7460.56,7472.8,7448.11,7513.83,7482.12,7512.5,7489.62,7445.96,7481.97,7489.84,7486.69,7476.71,7395.86,7361.77,7288.1,7477.9,7450.74,7502.5,7472.8,7513.83,TSMixer,PatchTST,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.0,0.01,0.02,0.03,7493.94,Informer
-2024-04-28,7492.31,7527.77,7453.0,7454.06,7481.64,7470.31,7526.84,7501.31,7531.63,7497.66,7466.42,7499.28,7485.05,7472.34,7496.09,7403.69,7365.99,7287.76,7486.34,7491.89,7527.5,7481.64,7526.84,TSMixer,PatchTST,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.02,0.01,0.01,0.0,0.01,0.02,0.03,7527.77,Informer
-2024-04-29,7498.57,7548.47,7486.26,7431.35,7495.67,7531.22,7528.19,7533.16,7564.23,7521.28,7480.11,7535.52,7457.69,7502.54,7512.45,7415.22,7369.91,7304.96,7503.48,7512.67,7537.5,7495.67,7533.16,TSMixer,RNN,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.02,0.0,0.01,0.0,0.01,0.02,0.03,7535.52,DilatedRNN
-2024-04-30,7495.75,7592.61,7496.31,7370.04,7501.83,7521.1,7525.03,7545.76,7570.16,7542.07,7482.28,7551.96,7438.24,7535.52,7529.03,7422.59,7374.53,7279.18,7509.82,7520.88,7532.5,7501.83,7545.76,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.01,0.0,0.02,0.02,0.03,7535.52,DLinear
-2024-05-06,7486.98,7575.81,7488.82,7356.33,7496.14,7475.81,7510.93,7539.36,7560.82,7539.02,7472.66,7538.92,7425.33,7523.91,7531.51,7425.09,7380.07,7292.73,7510.87,7504.35,7502.5,7496.14,7539.36,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.0,0.02,0.02,0.03,7504.35,DeepNPTS
-2024-05-07,7459.47,7522.92,7446.9,7293.06,7472.45,7416.02,7473.61,7500.68,7517.77,7513.82,7461.0,7495.62,7401.71,7499.71,7493.95,7423.25,7385.97,7269.02,7482.75,7427.8,7475.0,7472.45,7500.68,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.02,0.01,0.03,7473.61,PatchTST
-2024-05-08,7452.1,7498.92,7412.88,7266.95,7451.0,7393.7,7456.99,7466.12,7491.15,7492.59,7451.26,7466.67,7400.62,7459.54,7476.41,7422.86,7391.94,7251.08,7457.2,7413.32,7512.5,7451.0,7476.41,TSMixer,NLinear,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.02,0.01,0.0,0.03,0.02,0.03,7498.92,Informer
-2024-05-09,7507.17,7481.83,7470.25,7276.28,7474.45,7402.05,7487.83,7516.03,7551.66,7514.2,7457.1,7515.92,7427.3,7461.2,7499.92,7434.42,7398.46,7258.22,7479.38,7489.75,7505.0,7474.45,7516.03,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.03,0.01,0.03,7507.17,NHITS
-2024-05-10,7513.2,7490.87,7449.28,7300.66,7485.6,7421.4,7469.69,7498.91,7523.2,7512.77,7449.59,7501.96,7431.21,7468.93,7501.23,7437.45,7405.6,7252.87,7475.75,7494.29,7477.5,7469.69,7501.23,PatchTST,NLinear,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.02,0.01,0.03,7475.75,TiDE
-2024-05-11,7483.99,7472.65,7416.67,7318.22,7474.62,7444.24,7451.12,7474.21,7495.17,7500.56,7435.79,7469.77,7434.19,7468.85,7475.62,7438.88,7412.61,7258.36,7463.97,7463.24,7475.0,7451.12,7475.62,PatchTST,NLinear,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.02,0.01,0.03,7474.62,TSMixer
-2024-05-13,7492.02,7469.96,7417.52,7332.23,7473.29,7459.2,7460.44,7485.61,7499.11,7486.36,7435.56,7462.95,7449.81,7487.18,7477.07,7448.36,7419.8,7278.9,7470.81,7462.39,7485.0,7460.44,7485.61,PatchTST,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.02,0.01,0.03,7485.61,RNN
-2024-05-14,7485.84,7471.29,7430.62,7342.64,7479.87,7489.41,7462.96,7479.66,7511.71,7483.63,7457.97,7468.02,7482.82,7489.58,7495.8,7450.75,7428.02,7325.6,7475.31,7470.97,7507.5,7462.96,7495.8,PatchTST,NLinear,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.02,0.01,0.02,7511.71,GRU
-2024-05-15,7494.66,7527.76,7462.12,7357.79,7498.05,7496.8,7487.66,7508.05,7541.39,7503.29,7486.74,7493.37,7518.69,7500.46,7501.49,7471.96,7432.85,7329.11,7495.48,7474.04,7510.0,7487.66,7508.05,PatchTST,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.01,0.02,7508.05,RNN
-2024-05-16,7486.32,7547.16,7461.93,7375.92,7505.98,7500.59,7506.94,7517.17,7536.9,7516.66,7504.73,7500.54,7524.96,7515.96,7497.25,7475.93,7436.31,7369.26,7509.39,7487.81,7525.0,7497.25,7517.17,NLinear,RNN,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.01,0.02,7524.96,MLP
-2024-05-17,7495.27,7547.66,7484.75,7363.66,7512.85,7512.16,7527.19,7534.39,7561.08,7537.95,7517.79,7529.81,7511.87,7526.4,7519.39,7487.89,7438.31,7337.0,7520.01,7495.98,7587.5,7512.85,7534.39,TSMixer,RNN,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.03,0.02,0.03,7561.08,GRU
-2024-05-20,7560.91,7627.52,7571.38,7382.25,7551.36,7540.14,7599.44,7615.53,7644.78,7640.54,7541.9,7609.56,7537.17,7538.88,7591.33,7608.87,7440.06,7344.79,7572.66,7580.6,7670.0,7551.36,7615.53,TSMixer,RNN,0.01,0.01,0.01,0.02,0.01,0.01,0.01,0.02,0.0,0.01,0.01,0.01,0.02,0.01,0.02,0.02,0.0,0.04,0.03,0.04,7644.78,GRU
-2024-05-21,7658.46,7702.18,7684.89,7412.29,7615.56,7572.75,7679.05,7723.06,7740.26,7767.59,7554.82,7719.04,7600.38,7599.58,7671.84,7656.84,7442.83,7387.49,7645.65,7667.59,7677.5,7615.56,7723.06,TSMixer,RNN,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.02,0.01,0.03,0.03,0.04,7679.05,PatchTST
-2024-05-22,7658.66,7694.97,7674.66,7429.14,7638.08,7568.24,7632.54,7712.51,7717.9,7771.7,7542.16,7724.91,7588.67,7629.96,7679.33,7656.76,7446.85,7412.06,7659.85,7672.34,7677.5,7632.54,7712.51,PatchTST,RNN,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.02,0.01,0.03,0.03,0.03,7679.33,NLinear
-2024-05-23,7642.4,7676.73,7676.06,7435.77,7634.23,7552.03,7612.69,7712.66,7718.55,7767.44,7536.28,7722.24,7566.67,7651.84,7671.41,7581.26,7452.62,7439.62,7656.34,7653.24,7645.0,7612.69,7712.66,PatchTST,RNN,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.02,0.03,0.03,0.03,7642.4,NHITS
-2024-05-24,7609.85,7677.56,7617.06,7413.75,7605.9,7513.65,7579.41,7654.46,7660.86,7738.27,7534.54,7674.64,7539.22,7650.42,7640.53,7529.25,7459.19,7459.08,7620.13,7598.74,7630.0,7579.41,7654.46,PatchTST,RNN,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.02,0.01,0.01,0.03,0.02,0.02,7620.13,TiDE
-2024-05-27,7602.61,7604.74,7602.11,7442.68,7591.02,7509.58,7585.75,7619.81,7655.25,7679.82,7535.6,7648.6,7529.26,7610.52,7622.08,7536.4,7467.0,7448.39,7606.81,7607.49,7650.0,7585.75,7622.08,PatchTST,NLinear,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.02,0.01,0.02,0.01,0.0,0.03,0.02,0.03,7648.6,DilatedRNN
-2024-05-28,7621.53,7579.11,7632.67,7460.22,7604.76,7536.8,7604.37,7666.65,7688.61,7713.19,7536.61,7676.49,7535.3,7602.96,7628.69,7561.36,7475.23,7472.05,7623.2,7637.36,7690.0,7604.37,7666.65,PatchTST,RNN,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.02,0.02,0.02,0.02,0.0,0.03,0.03,0.03,7688.61,GRU
-2024-05-29,7660.31,7564.95,7690.2,7542.13,7642.38,7556.52,7635.98,7722.5,7740.62,7758.82,7553.88,7726.35,7564.89,7633.76,7662.29,7584.71,7483.88,7537.41,7655.15,7643.97,7690.0,7635.98,7722.5,PatchTST,RNN,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.02,0.01,0.02,0.01,0.02,0.02,0.01,0.02,0.03,0.02,7690.2,LSTM
-2024-05-30,7659.5,7553.99,7679.95,7592.99,7652.8,7541.2,7625.44,7716.96,7723.41,7779.81,7572.33,7718.33,7566.44,7647.5,7675.07,7605.57,7492.21,7596.18,7666.5,7566.29,7716.67,7625.44,7716.96,PatchTST,RNN,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.02,0.02,0.02,0.01,0.02,0.02,0.01,0.02,0.03,0.02,7716.96,RNN
-2024-05-31,7676.04,7592.94,7721.02,7620.88,7669.84,7558.63,7640.45,7755.05,7767.45,7825.41,7589.97,7745.55,7604.26,7679.07,7714.9,7641.48,7499.87,7620.5,7694.39,7561.95,7740.0,7640.45,7755.05,PatchTST,RNN,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.02,0.02,0.02,0.01,0.02,0.02,0.01,0.02,0.03,0.02,7745.55,DilatedRNN
-2024-06-03,7690.19,7602.5,7744.59,7643.27,7692.21,7576.14,7643.83,7774.9,7783.75,7838.53,7598.89,7768.8,7652.97,7688.61,7744.76,7658.11,7507.05,7660.01,7713.29,7635.55,7720.0,7643.83,7774.9,PatchTST,RNN,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.02,0.01,0.01,0.01,0.02,0.02,0.02,0.01,0.03,0.01,7713.29,TiDE
-2024-06-04,7676.58,7604.72,7709.75,7625.74,7686.42,7586.88,7633.54,7732.22,7745.49,7822.4,7601.66,7739.83,7638.93,7670.32,7704.17,7640.85,7512.9,7624.79,7702.71,7650.31,7650.0,7633.54,7732.22,PatchTST,RNN,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.01,0.02,0.0,0.02,0.0,7650.31,DeepNPTS
-2024-06-05,7617.42,7585.56,7608.16,7521.1,7636.97,7592.04,7619.76,7642.54,7645.94,7710.69,7592.1,7655.94,7600.55,7656.55,7622.66,7582.8,7516.58,7580.1,7640.7,7558.49,7620.0,7619.76,7642.54,PatchTST,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,7619.76,PatchTST
-2024-06-06,7627.55,7601.65,7581.97,7498.69,7602.71,7600.4,7617.53,7613.13,7635.79,7570.58,7579.57,7631.12,7591.91,7629.57,7600.04,7577.28,7519.33,7549.89,7594.44,7584.87,7600.0,7594.44,7617.53,TiDE,PatchTST,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7600.04,NLinear
-2024-06-07,7620.12,7576.07,7555.8,7496.98,7587.86,7584.3,7616.1,7584.17,7614.55,7535.23,7568.33,7605.75,7595.99,7578.05,7587.0,7574.39,7522.1,7512.69,7578.68,7597.59,7606.67,7578.68,7616.1,TiDE,PatchTST,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,7605.75,DilatedRNN
-2024-06-11,7615.42,7561.42,7572.2,7494.23,7592.89,7608.43,7623.98,7598.93,7636.73,7560.02,7573.55,7615.37,7596.81,7575.98,7597.5,7578.83,7525.05,7527.02,7589.47,7608.48,7620.0,7589.47,7623.98,TiDE,PatchTST,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.02,0.01,0.01,7623.98,PatchTST
-2024-06-12,7611.62,7553.93,7591.31,7496.85,7606.43,7639.72,7624.18,7636.33,7654.1,7637.44,7589.62,7633.55,7569.36,7605.97,7609.05,7591.22,7527.96,7539.77,7601.45,7616.41,7673.33,7601.45,7636.33,TiDE,RNN,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.02,0.01,0.01,0.01,0.0,0.01,0.0,0.02,0.02,0.02,7654.1,GRU
-2024-06-13,7655.61,7603.46,7674.73,7519.39,7641.07,7638.81,7672.45,7713.7,7731.49,7805.59,7621.73,7705.76,7603.03,7659.96,7673.02,7621.81,7532.56,7584.4,7645.12,7648.14,7690.0,7641.07,7713.7,TSMixer,RNN,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.02,0.02,0.01,7674.73,LSTM
-2024-06-14,7662.58,7670.16,7685.94,7575.84,7660.44,7647.96,7682.6,7725.29,7731.85,7828.61,7646.61,7720.59,7639.52,7679.22,7692.25,7660.54,7536.97,7625.65,7665.64,7660.87,7690.0,7660.44,7725.29,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.02,0.01,0.02,0.01,7692.25,NLinear
-2024-06-17,7671.29,7647.84,7684.84,7597.49,7665.59,7665.5,7691.83,7715.76,7728.39,7830.33,7664.51,7720.84,7657.3,7667.72,7690.86,7643.12,7541.43,7646.18,7679.22,7629.43,7656.67,7665.59,7715.76,TSMixer,RNN,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.02,0.0,7657.3,MLP
-2024-06-18,7654.17,7665.9,7629.33,7606.86,7644.06,7636.13,7666.63,7667.54,7672.72,7798.64,7652.07,7680.42,7619.72,7641.58,7656.95,7592.23,7545.62,7627.04,7657.63,7638.64,7656.67,7644.06,7667.54,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.02,0.01,0.01,0.0,7656.95,NLinear
-2024-06-19,7727.11,7640.08,7638.93,7578.59,7636.68,7591.02,7659.72,7664.19,7690.23,7756.19,7637.89,7681.55,7618.03,7634.64,7654.33,7591.34,7548.8,7635.99,7636.73,7637.2,7670.0,7636.68,7664.19,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.02,0.0,7664.19,RNN
-2024-06-20,7756.39,7659.25,7656.39,7564.65,7645.7,7595.62,7650.33,7687.96,7706.14,7773.77,7634.08,7701.54,7642.69,7644.28,7666.27,7593.11,7552.24,7671.8,7632.11,7650.3,7680.0,7632.11,7687.96,TiDE,RNN,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.02,0.02,0.0,7687.96,RNN
-2024-06-21,7726.67,7685.7,7669.65,7563.31,7659.75,7610.74,7654.26,7698.64,7717.54,7814.91,7636.12,7712.27,7668.85,7668.87,7677.59,7596.45,7556.53,7678.47,7647.91,7660.17,7680.0,7647.91,7698.64,TiDE,RNN,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.02,0.02,0.02,0.0,7678.47,MLPMultivariate
-2024-06-24,7694.64,7672.12,7667.38,7570.59,7668.28,7625.21,7646.98,7705.2,7713.55,7819.28,7629.2,7707.03,7645.71,7680.38,7684.28,7592.7,7561.85,7657.72,7666.29,7648.92,7680.0,7646.98,7705.2,PatchTST,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.02,0.01,0.02,0.0,7680.38,DLinear
-2024-06-25,7656.97,7685.36,7667.81,7584.83,7669.49,7652.55,7639.3,7702.86,7714.04,7804.54,7638.43,7698.53,7635.15,7695.28,7680.91,7592.76,7567.32,7651.89,7677.07,7634.02,7693.33,7639.3,7702.86,PatchTST,RNN,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.01,7695.28,DLinear
-2024-06-26,7663.05,7700.56,7688.21,7592.06,7675.82,7671.93,7645.04,7717.94,7733.98,7810.16,7655.06,7714.51,7645.3,7714.43,7695.17,7603.42,7572.47,7672.88,7691.75,7632.18,7716.67,7645.04,7717.94,PatchTST,RNN,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.02,0.01,7717.94,RNN
-2024-06-27,7677.4,7733.3,7717.34,7581.76,7688.27,7684.8,7672.33,7746.99,7762.31,7828.62,7679.04,7743.03,7653.37,7703.94,7708.81,7629.43,7577.01,7688.28,7699.69,7625.66,7723.33,7672.33,7746.99,PatchTST,RNN,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.02,0.02,0.0,7717.34,LSTM
-2024-06-28,7677.32,7757.11,7720.51,7600.88,7694.57,7678.53,7677.87,7750.27,7761.7,7842.71,7690.04,7751.73,7651.4,7685.27,7712.89,7630.63,7581.37,7685.2,7704.47,7616.63,7730.0,7677.87,7750.27,PatchTST,RNN,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.02,0.01,7720.51,LSTM
-2024-07-01,7707.93,7783.31,7729.01,7658.78,7698.83,7678.79,7708.14,7757.56,7769.41,7858.08,7695.38,7759.5,7666.04,7688.93,7720.96,7649.1,7586.13,7704.59,7715.52,7664.76,7763.33,7698.83,7757.56,TSMixer,RNN,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.01,7759.5,DilatedRNN
-2024-07-02,7751.55,7828.43,7773.13,7771.68,7722.98,7680.78,7739.17,7797.09,7808.54,7880.75,7688.06,7793.55,7695.02,7722.0,7756.72,7715.2,7592.57,7759.12,7744.77,7741.74,7780.0,7722.98,7797.09,TSMixer,RNN,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.0,0.02,0.0,7773.13,LSTM
-2024-07-03,7762.43,7821.1,7785.83,7768.14,7737.08,7688.67,7737.38,7805.72,7814.84,7884.52,7671.07,7805.74,7699.38,7736.65,7780.01,7781.53,7600.68,7781.87,7747.47,7776.13,7800.0,7737.08,7805.72,TSMixer,RNN,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.02,0.01,0.0,0.03,0.0,7805.72,RNN
-2024-07-04,7786.08,7820.99,7808.32,7778.61,7757.09,7698.67,7767.39,7826.63,7834.39,7886.31,7664.84,7824.24,7716.39,7755.69,7793.56,7744.54,7607.4,7810.21,7767.25,7795.52,7800.0,7757.09,7826.63,TSMixer,RNN,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.02,0.01,0.0,0.02,0.0,7795.52,DeepNPTS
-2024-07-05,7786.85,7820.6,7802.07,7857.05,7765.0,7704.08,7775.88,7824.34,7824.23,7867.37,7675.64,7822.93,7717.42,7771.03,7787.48,7722.59,7614.78,7784.03,7781.0,7773.13,7766.67,7765.0,7824.34,TSMixer,RNN,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.0,7765.0,TSMixer
-2024-07-08,7750.86,7775.85,7756.63,7790.97,7745.48,7728.45,7735.1,7771.43,7782.12,7830.41,7681.79,7783.1,7680.52,7766.86,7756.73,7673.49,7623.7,7743.8,7760.92,7733.4,7746.67,7735.1,7771.43,PatchTST,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.02,0.0,7745.48,TSMixer
-2024-07-09,7722.96,7771.44,7738.08,7700.89,7723.15,7740.89,7719.52,7758.3,7770.58,7808.82,7683.28,7764.36,7650.72,7738.94,7735.36,7674.87,7632.98,7718.29,7736.34,7708.61,7720.0,7719.52,7758.3,PatchTST,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.01,0.0,7719.52,PatchTST
-2024-07-10,7678.34,7688.29,7704.44,7589.11,7701.77,7712.37,7697.39,7727.91,7738.12,7775.17,7671.93,7736.38,7618.9,7709.76,7703.35,7668.79,7641.77,7677.77,7704.73,7692.53,7693.33,7697.39,7727.91,PatchTST,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7692.53,DeepNPTS
-2024-07-11,7648.69,7674.48,7673.89,7581.47,7676.21,7708.96,7680.71,7693.04,7711.88,7753.58,7666.8,7706.51,7619.0,7682.29,7677.71,7681.14,7649.18,7661.22,7672.21,7680.78,7686.67,7672.21,7693.04,TiDE,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.0,0.0,7682.29,DLinear
-2024-07-12,7669.61,7650.62,7671.48,7621.69,7665.51,7658.81,7675.2,7701.66,7714.63,7765.84,7667.84,7701.91,7626.23,7678.42,7672.11,7680.77,7656.73,7648.62,7658.77,7681.25,7680.0,7658.77,7701.66,TiDE,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.0,0.0,7680.77,TFT
-2024-07-15,7674.74,7636.96,7662.9,7660.4,7664.41,7649.86,7662.4,7692.31,7707.55,7758.14,7666.8,7694.71,7642.93,7675.73,7670.65,7683.32,7663.66,7658.05,7658.65,7685.69,7680.0,7658.65,7692.31,TiDE,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,7683.32,TFT
-2024-07-16,7651.22,7665.87,7666.36,7679.77,7669.12,7662.39,7662.32,7694.89,7712.67,7768.03,7660.25,7693.56,7646.65,7667.06,7679.83,7685.51,7667.3,7657.69,7669.74,7726.9,7666.67,7662.32,7694.89,PatchTST,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,7666.36,LSTM
-2024-07-17,7637.9,7660.13,7645.18,7660.46,7665.03,7655.32,7661.53,7680.92,7691.54,7757.11,7655.57,7673.35,7649.5,7651.11,7662.8,7670.56,7667.2,7655.92,7665.27,7701.17,7660.0,7661.53,7680.92,PatchTST,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,7660.13,Informer
-2024-07-18,7661.33,7666.53,7639.5,7683.98,7661.71,7662.09,7657.96,7670.44,7689.36,7759.37,7647.55,7671.03,7659.78,7652.43,7661.6,7657.73,7666.66,7654.79,7657.27,7689.07,7656.67,7657.27,7670.44,TiDE,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,7657.27,TiDE
-2024-07-19,7665.03,7654.06,7635.24,7668.78,7662.77,7660.37,7654.84,7669.45,7686.07,7754.96,7643.93,7669.79,7665.58,7643.64,7663.61,7649.73,7666.01,7657.81,7657.13,7685.6,7656.67,7654.84,7669.45,PatchTST,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,7657.13,TiDE
-2024-07-22,7649.73,7644.27,7636.49,7644.6,7665.12,7666.42,7654.45,7668.51,7688.3,7764.49,7644.52,7672.99,7672.46,7658.74,7658.42,7642.28,7666.42,7664.4,7656.29,7667.09,7640.0,7654.45,7668.51,PatchTST,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.0,7642.28,TFT
-2024-07-23,7615.23,7645.56,7610.57,7655.94,7652.1,7662.34,7634.09,7645.28,7662.61,7733.56,7646.66,7650.71,7654.92,7659.24,7642.47,7624.17,7666.82,7659.42,7638.33,7656.65,7623.33,7634.09,7652.1,PatchTST,TSMixer,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,7624.17,TFT
-2024-07-24,7597.53,7650.06,7590.27,7719.11,7634.82,7637.72,7616.98,7624.78,7646.08,7661.27,7642.03,7625.71,7641.44,7656.11,7626.19,7609.78,7665.78,7651.82,7623.34,7656.74,7623.33,7616.98,7634.82,PatchTST,TSMixer,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,7623.34,TiDE
-2024-07-25,7608.65,7635.91,7593.96,7735.25,7627.76,7630.19,7616.05,7628.94,7653.01,7653.68,7631.24,7626.97,7640.3,7666.61,7627.16,7611.94,7663.56,7653.03,7621.39,7660.64,7616.67,7616.05,7628.94,PatchTST,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.0,7616.05,PatchTST
-2024-07-26,7617.22,7632.98,7583.26,7719.98,7622.01,7624.53,7601.33,7616.61,7641.8,7622.3,7619.09,7615.34,7640.72,7653.16,7615.57,7609.45,7661.1,7643.86,7611.62,7667.77,7606.67,7601.33,7622.01,PatchTST,TSMixer,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,7609.45,TFT
-2024-07-29,7632.45,7609.45,7571.18,7713.09,7611.68,7620.21,7594.82,7607.42,7631.3,7611.93,7608.13,7602.7,7638.04,7621.31,7607.55,7595.9,7657.72,7625.12,7607.82,7630.84,7593.33,7594.82,7611.68,PatchTST,TSMixer,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.02,0.01,0.0,7594.82,PatchTST
-2024-07-30,7621.05,7605.67,7554.07,7693.67,7599.03,7601.6,7588.37,7595.38,7615.72,7591.75,7594.35,7583.41,7629.58,7602.19,7598.93,7574.65,7653.51,7600.72,7596.26,7617.22,7593.33,7588.37,7599.03,PatchTST,TSMixer,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,7594.35,BiTCN
-2024-07-31,7587.46,7604.13,7557.67,7676.02,7593.74,7595.79,7589.72,7594.85,7621.85,7591.03,7581.07,7582.76,7605.31,7587.62,7602.61,7569.93,7649.69,7571.38,7593.87,7608.1,7583.33,7589.72,7602.61,PatchTST,NLinear,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,7582.76,DilatedRNN
-2024-08-01,7576.43,7594.77,7542.38,7676.91,7583.79,7575.55,7576.5,7581.67,7606.06,7578.42,7566.83,7567.78,7578.68,7581.86,7588.04,7561.38,7647.13,7543.93,7580.26,7601.32,7620.0,7576.5,7588.04,PatchTST,NLinear,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,7606.06,GRU
-2024-08-02,7616.25,7599.38,7601.53,7702.78,7607.2,7603.66,7609.55,7638.54,7668.72,7714.11,7574.54,7622.81,7587.85,7601.98,7625.29,7605.34,7647.13,7558.22,7606.16,7607.22,7623.33,7606.16,7638.54,TiDE,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,7622.81,DilatedRNN
-2024-08-05,7621.53,7592.47,7595.34,7698.87,7621.36,7617.55,7611.03,7638.07,7653.23,7735.43,7591.9,7625.9,7595.08,7607.4,7626.78,7606.12,7648.05,7577.72,7617.05,7605.43,7610.0,7611.03,7638.07,PatchTST,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.0,0.0,7611.03,PatchTST
-2024-08-06,7614.82,7598.47,7578.08,7696.71,7616.64,7609.5,7607.33,7612.45,7636.6,7706.2,7602.73,7605.37,7605.46,7622.93,7609.92,7603.31,7648.18,7593.61,7621.72,7609.77,7596.67,7607.33,7621.72,PatchTST,TiDE,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7598.47,Informer
-2024-08-07,7600.42,7592.13,7560.49,7700.19,7603.22,7600.74,7594.67,7605.4,7620.27,7632.15,7602.99,7587.78,7602.43,7620.43,7599.1,7588.53,7647.25,7581.33,7607.23,7602.89,7586.67,7594.67,7607.23,PatchTST,TiDE,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7587.78,DilatedRNN
-2024-08-08,7582.46,7579.27,7547.9,7691.99,7590.51,7583.07,7585.2,7581.87,7610.47,7580.54,7596.98,7572.43,7580.94,7599.9,7587.52,7592.21,7644.23,7643.29,7591.49,7595.2,7583.33,7581.87,7591.49,RNN,TiDE,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7583.07,TSMixerx
-2024-08-09,7572.3,7579.08,7544.76,7685.98,7581.03,7579.99,7585.11,7579.56,7609.39,7579.0,7593.55,7571.36,7577.72,7584.52,7578.71,7580.27,7640.76,7661.68,7580.79,7613.95,7583.33,7578.71,7585.11,NLinear,PatchTST,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7584.52,DLinear
-2024-08-12,7565.19,7562.29,7545.41,7676.57,7579.53,7568.37,7583.51,7586.38,7610.8,7581.76,7591.14,7573.16,7571.12,7579.25,7574.68,7569.9,7637.37,7675.83,7579.89,7606.85,7586.67,7574.68,7586.38,NLinear,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7586.38,RNN
-2024-08-13,7563.62,7566.02,7550.8,7670.48,7579.66,7581.39,7587.7,7591.05,7616.26,7595.69,7589.46,7581.39,7578.19,7586.37,7580.72,7572.45,7634.98,7685.1,7584.15,7605.62,7593.33,7579.66,7591.05,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7591.05,RNN
-2024-08-14,7576.39,7581.33,7560.34,7675.32,7581.9,7581.96,7593.26,7601.19,7625.12,7601.06,7585.96,7582.96,7591.15,7583.67,7594.52,7589.33,7632.88,7686.85,7588.09,7596.56,7590.0,7581.9,7601.19,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7589.33,TFT
-2024-08-15,7577.48,7580.18,7554.1,7682.5,7580.27,7581.07,7588.06,7594.36,7617.3,7594.63,7577.61,7574.04,7585.9,7573.04,7589.19,7595.72,7630.35,7676.25,7584.21,7588.85,7583.33,7580.27,7594.36,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7584.21,TiDE
-2024-08-16,7575.3,7564.63,7545.8,7686.68,7575.85,7576.91,7580.0,7585.32,7609.72,7583.25,7570.84,7568.21,7574.59,7570.58,7584.68,7589.57,7627.38,7665.61,7577.83,7587.43,7583.33,7575.85,7585.32,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7583.25,TCN
-2024-08-19,7583.78,7564.47,7547.04,7693.78,7576.66,7590.53,7583.24,7587.57,7611.94,7580.78,7566.85,7568.77,7576.56,7563.43,7580.76,7592.1,7624.46,7672.57,7577.68,7592.79,7570.0,7576.66,7587.57,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.01,7568.77,DilatedRNN
-2024-08-20,7572.15,7547.01,7526.81,7671.45,7568.6,7580.24,7569.61,7567.35,7591.95,7568.04,7561.53,7550.82,7574.94,7559.89,7564.28,7585.51,7621.06,7668.15,7564.85,7593.95,7570.0,7564.28,7569.61,NLinear,PatchTST,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7569.61,PatchTST
-2024-08-21,7573.42,7547.32,7530.82,7666.64,7567.89,7575.96,7578.4,7571.05,7598.86,7567.66,7559.81,7551.3,7579.86,7556.56,7568.24,7577.28,7617.48,7658.24,7567.96,7595.48,7570.0,7567.89,7578.4,TSMixer,PatchTST,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7571.05,RNN
-2024-08-22,7573.07,7553.0,7529.61,7668.62,7570.14,7576.54,7578.19,7572.59,7597.05,7564.06,7557.67,7549.96,7582.79,7558.09,7571.36,7573.0,7613.63,7649.21,7570.82,7596.41,7590.0,7570.14,7578.19,TSMixer,PatchTST,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,7596.41,DeepNPTS
-2024-08-23,7591.57,7570.94,7560.14,7670.81,7585.53,7577.36,7595.08,7597.17,7628.08,7602.95,7563.0,7578.88,7588.25,7580.61,7588.53,7588.33,7610.53,7651.94,7586.39,7599.99,7583.33,7585.53,7597.17,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,7585.53,TSMixer
-2024-08-26,7585.94,7559.82,7544.76,7661.83,7584.95,7583.38,7584.8,7588.16,7607.8,7587.92,7569.26,7568.6,7581.87,7588.27,7584.7,7584.03,7607.22,7649.99,7582.57,7594.95,7583.33,7582.57,7588.16,TiDE,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,7583.38,TSMixerx
-2024-08-27,7583.67,7562.52,7548.9,7658.7,7583.01,7588.14,7583.17,7588.62,7614.19,7590.1,7572.3,7571.03,7577.78,7596.16,7581.18,7582.94,7603.87,7646.62,7585.5,7587.22,7610.0,7581.18,7588.62,NLinear,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,7614.19,GRU
-2024-08-28,7608.75,7573.37,7587.12,7673.3,7599.94,7608.28,7606.73,7628.21,7652.47,7664.54,7591.65,7610.38,7596.44,7602.33,7610.44,7591.02,7601.62,7649.51,7604.31,7595.06,7610.0,7599.94,7628.21,TSMixer,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,7610.38,DilatedRNN
-2024-08-29,7610.24,7581.95,7579.49,7667.2,7606.13,7611.64,7601.7,7614.17,7638.98,7657.09,7608.52,7604.72,7599.74,7592.46,7610.07,7593.12,7599.89,7651.06,7610.39,7598.34,7613.33,7601.7,7614.17,PatchTST,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,7614.17,RNN
-2024-08-30,7614.03,7597.63,7586.96,7655.72,7609.34,7627.82,7609.66,7622.84,7647.73,7683.32,7625.2,7611.68,7604.11,7604.98,7607.4,7605.44,7598.84,7655.51,7616.44,7604.42,7606.67,7607.4,7622.84,NLinear,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,7607.4,NLinear
-2024-09-02,7605.77,7607.48,7574.01,7657.55,7606.13,7623.34,7601.08,7617.14,7632.79,7647.45,7631.48,7601.88,7607.32,7602.92,7610.32,7598.03,7597.7,7649.4,7604.07,7593.73,7583.33,7601.08,7617.14,PatchTST,RNN,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.01,7574.01,LSTM
-2024-09-03,7585.53,7591.18,7541.28,7616.14,7585.56,7597.08,7575.22,7577.4,7602.02,7584.32,7620.22,7566.19,7593.28,7584.09,7584.79,7569.87,7596.02,7632.57,7579.08,7589.84,7562.5,7575.22,7585.56,PatchTST,TSMixer,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,7566.19,DilatedRNN
-2024-09-04,7568.37,7571.94,7515.98,7610.57,7565.79,7581.26,7562.25,7558.08,7581.85,7545.27,7604.56,7535.46,7576.39,7572.94,7565.45,7557.33,7593.59,7618.83,7560.98,7584.89,7535.0,7558.08,7565.79,RNN,TSMixer,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,7535.46,DilatedRNN
-2024-09-05,7543.76,7554.78,7480.27,7596.32,7542.46,7560.43,7542.41,7526.06,7549.62,7514.26,7580.6,7496.51,7554.42,7552.65,7529.62,7533.45,7589.9,7609.7,7536.19,7604.62,7475.0,7526.06,7542.46,RNN,TSMixer,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.02,0.01,0.01,0.01,0.01,0.01,0.02,0.02,0.02,7480.27,LSTM
-2024-09-06,7486.44,7535.38,7399.34,7579.63,7490.15,7530.32,7484.73,7455.8,7477.09,7467.9,7540.47,7420.37,7514.24,7518.1,7469.82,7508.77,7582.87,7581.29,7483.85,7606.65,7442.5,7455.8,7490.15,RNN,TSMixer,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.01,0.01,0.02,0.01,0.01,0.01,0.01,0.0,0.02,0.02,0.02,7455.8,RNN
-2024-09-09,7470.01,7515.88,7364.62,7562.42,7446.27,7498.53,7460.67,7431.29,7457.63,7439.77,7496.35,7380.39,7490.04,7478.05,7433.33,7489.86,7574.18,7557.17,7447.27,7583.34,7422.5,7431.29,7460.67,RNN,PatchTST,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.02,0.01,0.01,0.01,0.01,0.0,0.02,0.02,0.02,7431.29,RNN
-2024-09-10,7451.33,7504.81,7339.71,7541.21,7420.48,7477.1,7438.77,7409.71,7440.34,7417.87,7462.64,7355.09,7458.54,7445.15,7421.15,7475.16,7564.85,7527.78,7425.45,7562.5,7412.5,7409.71,7438.77,RNN,PatchTST,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.02,0.01,0.01,0.01,0.01,0.0,0.02,0.02,0.02,7409.71,RNN
-2024-09-11,7430.97,7491.05,7330.97,7519.37,7404.28,7462.26,7420.89,7398.85,7436.14,7408.15,7431.14,7340.45,7432.55,7418.98,7415.28,7464.61,7555.64,7498.71,7417.44,7535.0,7387.5,7398.85,7420.89,RNN,PatchTST,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.02,0.01,0.01,0.01,0.01,0.0,0.02,0.02,0.02,7398.85,RNN
-2024-09-12,7398.0,7478.74,7303.67,7492.92,7382.31,7444.67,7391.09,7382.94,7411.65,7396.88,7397.14,7313.15,7402.38,7400.79,7386.94,7448.76,7545.54,7460.06,7395.3,7475.01,7380.0,7382.31,7395.3,TSMixer,TiDE,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.0,0.02,0.02,0.01,7382.31,TSMixer
-2024-09-13,7388.71,7460.03,7300.95,7464.13,7369.45,7424.93,7380.86,7379.25,7411.88,7392.85,7374.15,7309.57,7382.1,7401.54,7385.04,7438.55,7535.68,7439.73,7384.62,7442.51,7395.0,7369.45,7385.04,TSMixer,NLinear,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.02,0.01,7392.85,TCN
-2024-09-14,7403.95,7464.0,7318.92,7450.39,7380.9,7417.35,7390.87,7396.28,7429.07,7406.03,7371.11,7327.5,7375.1,7403.94,7403.98,7435.49,7527.23,7421.01,7393.19,7422.53,7375.0,7380.9,7403.98,TSMixer,NLinear,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.02,0.01,7375.1,MLP
-2024-09-18,7378.25,7450.51,7296.64,7420.48,7374.33,7397.35,7365.31,7369.65,7403.89,7405.31,7367.04,7307.74,7352.11,7382.56,7384.77,7418.46,7518.24,7376.68,7382.2,7412.61,7342.5,7365.31,7384.77,PatchTST,NLinear,0.01,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.02,0.0,7352.11,MLP
-2024-09-19,7345.8,7424.07,7273.03,7383.78,7351.39,7367.71,7348.02,7343.67,7377.78,7405.74,7364.49,7286.64,7340.79,7371.55,7346.23,7404.98,7508.11,7313.24,7357.08,7392.25,7325.0,7343.67,7357.08,RNN,TiDE,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.02,0.0,7313.24,MLPMultivariate
-2024-09-20,7331.34,7411.4,7261.47,7359.5,7333.1,7352.78,7340.24,7332.29,7366.28,7407.88,7366.1,7275.0,7339.29,7357.37,7336.36,7381.77,7497.51,7277.24,7337.64,7389.01,7340.0,7332.29,7340.24,RNN,PatchTST,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.02,0.01,7340.24,PatchTST
-2024-09-23,7350.17,7409.47,7273.6,7360.28,7338.57,7354.65,7355.94,7342.36,7383.53,7413.67,7372.86,7285.44,7350.24,7359.13,7346.39,7390.63,7488.3,7253.74,7336.25,7363.92,7310.0,7336.25,7355.94,TiDE,PatchTST,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.01,7285.44,DilatedRNN
-2024-09-24,7334.33,7387.52,7250.34,7333.1,7321.56,7341.2,7323.07,7313.94,7351.58,7405.47,7369.32,7258.87,7332.21,7339.95,7319.59,7372.23,7478.05,7233.88,7309.53,7330.44,7280.0,7309.53,7323.07,TiDE,PatchTST,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.01,0.03,0.01,7258.87,DilatedRNN
-2024-09-25,7331.68,7358.95,7233.76,7294.82,7295.02,7325.46,7313.92,7295.67,7333.57,7389.7,7358.18,7239.93,7329.29,7318.18,7284.22,7352.1,7466.86,7208.89,7292.13,7300.88,7315.0,7284.22,7313.92,NLinear,PatchTST,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.02,0.01,7313.92,PatchTST
-2024-09-26,7383.23,7354.28,7258.63,7322.43,7311.57,7337.57,7346.59,7334.2,7368.18,7395.19,7360.13,7262.58,7348.37,7337.87,7321.63,7365.79,7457.88,7369.88,7315.96,7309.91,7310.0,7311.57,7346.59,TSMixer,PatchTST,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.02,0.01,7309.91,DeepNPTS
-2024-09-27,7395.39,7342.84,7252.57,7349.44,7311.78,7332.55,7328.61,7316.26,7355.87,7383.46,7355.37,7250.85,7333.29,7314.8,7318.06,7363.19,7449.12,7426.95,7307.61,7323.36,7310.0,7307.61,7328.61,TiDE,PatchTST,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.02,0.02,7311.78,TSMixer
-2024-09-29,7404.69,7328.01,7255.95,7412.08,7310.44,7339.92,7335.89,7322.03,7360.7,7391.28,7348.53,7255.08,7324.23,7308.09,7314.78,7356.94,7440.11,7459.6,7314.78,7323.36,7365.0,7310.44,7335.89,TSMixer,PatchTST,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7360.7,GRU
-2024-09-30,7426.12,7341.11,7300.09,7456.35,7351.28,7360.36,7371.79,7386.81,7413.94,7423.46,7354.23,7305.8,7350.79,7337.02,7372.67,7375.01,7433.62,7526.69,7362.78,7362.27,7420.0,7351.28,7386.81,TSMixer,RNN,0.01,0.0,0.01,0.01,0.01,0.0,0.02,0.01,0.0,0.02,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.0,0.0,0.01,7423.46,TCN
-2024-10-08,7406.37,7396.05,7363.97,7515.23,7401.51,7391.97,7409.69,7428.8,7463.3,7450.62,7382.85,7368.46,7376.8,7366.11,7426.31,7404.72,7429.4,7614.2,7412.17,7419.09,7602.5,7401.51,7428.8,TSMixer,RNN,0.02,0.02,0.03,0.03,0.03,0.03,0.03,0.03,0.02,0.03,0.03,0.02,0.03,0.03,0.03,0.03,0.02,0.01,0.02,0.0,7614.2,MLPMultivariate
-2024-10-09,7577.33,7574.57,7625.6,7661.65,7544.47,7607.6,7631.74,7679.46,7699.37,7713.1,7580.36,7635.88,7586.54,7484.3,7607.45,7604.1,7429.48,7766.63,7573.71,7602.24,7602.5,7544.47,7679.46,TSMixer,RNN,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.02,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.02,0.02,7602.24,DeepNPTS
-2024-10-10,7576.0,7601.33,7581.64,7719.31,7608.25,7677.35,7632.34,7639.99,7633.43,7766.71,7661.03,7634.72,7640.36,7542.83,7609.24,7617.74,7429.69,7786.16,7604.18,7602.37,7585.0,7604.18,7639.99,TiDE,RNN,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.02,0.02,0.02,0.03,7581.64,LSTM
-2024-10-11,7631.65,7624.48,7575.07,7766.2,7609.2,7714.27,7629.44,7599.98,7624.22,7788.7,7691.29,7618.88,7651.66,7572.18,7587.86,7623.61,7429.71,7764.22,7600.31,7584.85,7575.0,7587.86,7629.44,NLinear,PatchTST,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.02,0.02,0.03,0.03,0.02,0.02,7575.07,LSTM
-2024-10-12,7653.07,7598.33,7548.5,7847.1,7585.18,7706.53,7607.08,7603.88,7602.92,7740.27,7700.39,7589.1,7663.18,7571.87,7574.65,7629.57,7429.59,7716.69,7568.07,7574.84,7576.67,7568.07,7607.08,TiDE,PatchTST,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.02,0.02,0.02,0.04,0.02,0.02,7574.84,DeepNPTS
-2024-10-14,7691.78,7608.75,7548.03,7871.49,7552.13,7690.78,7583.72,7553.81,7607.37,7730.31,7696.8,7583.52,7677.65,7557.6,7562.15,7651.37,7429.44,7670.88,7546.73,7576.1,7565.0,7546.73,7583.72,TiDE,PatchTST,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.02,0.02,0.02,0.04,0.02,0.01,7562.15,NLinear
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-2024-11-11,7385.33,7367.98,7315.88,7312.9,7378.33,7404.64,7387.22,7388.8,7424.18,7437.92,7365.14,7305.25,7419.91,7362.62,7385.12,7370.33,7356.28,7474.63,7378.79,7380.81,7340.0,7378.33,7388.8,TSMixer,RNN,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.01,0.0,0.0,0.02,7356.28,StemGNN
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+2024-01-30,7314.25,7335.76,7255.32,7375.61,7296.98,7304.24,7288.53,7316.8,7357.77,7406.36,7330.32,7284.31,7336.16,7282.57,7305.31,7289.84,7306.12,7295.54,7297.12,7304.88,7315.0,0.12,0.03,0.23,0.24,0.35,0.0,0.41,0.43,0.6,0.81,0.29,0.13,0.3,0.33,0.14,0.22,1.26,0.84,0.11,0.26,7255.32,7406.36,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,7314.25,NHITS
+2024-01-31,7313.85,7333.85,7260.27,7376.32,7306.93,7303.91,7291.97,7327.11,7364.25,7409.82,7340.18,7298.74,7321.85,7294.2,7298.47,7302.1,7304.55,7299.61,7302.49,7313.87,7302.5,0.21,0.18,0.16,0.09,0.3,0.0,0.21,0.27,0.69,0.73,0.27,0.0,0.11,0.16,0.14,0.36,1.31,0.85,0.13,0.19,7260.27,7409.82,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,7302.49,TiDE
+2024-02-01,7297.0,7335.21,7250.98,7385.46,7298.8,7304.56,7284.13,7314.06,7351.66,7406.99,7334.74,7290.45,7319.52,7291.02,7287.84,7290.86,7303.42,7300.2,7301.71,7297.39,7275.0,0.13,0.23,0.06,0.02,0.18,0.0,0.09,0.08,0.75,0.63,0.52,0.01,0.31,0.08,0.1,0.52,1.51,1.21,0.09,0.04,7250.98,7385.46,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.02,0.02,0.0,0.0,7284.13,PatchTST
+2024-02-02,7275.51,7314.77,7233.06,7349.83,7272.41,7282.94,7263.86,7294.73,7330.68,7395.3,7306.72,7260.65,7289.64,7278.71,7262.57,7266.32,7300.92,7272.72,7280.73,7271.37,7242.5,0.18,0.26,0.07,0.04,0.16,0.0,0.2,0.04,0.76,0.58,0.54,0.06,0.19,0.13,0.1,0.43,1.65,1.02,0.35,0.04,7233.06,7349.83,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.02,0.01,0.01,0.0,7233.06,LSTM
+2024-02-04,7236.79,7273.36,7212.67,7264.81,7239.09,7232.82,7228.47,7267.82,7308.6,7378.92,7271.96,7228.76,7248.22,7249.75,7245.09,7227.47,7297.04,7210.26,7249.74,7287.39,7240.0,0.11,0.43,0.18,0.03,0.11,0.0,0.11,0.18,0.99,0.33,0.51,0.7,0.16,0.13,0.05,0.49,1.96,0.39,0.83,0.37,7210.26,7308.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.02,0.0,0.01,0.0,7239.09,TSMixer
+2024-02-05,7237.9,7254.36,7211.69,7215.08,7225.67,7197.03,7226.16,7266.29,7310.45,7374.66,7241.34,7223.16,7238.12,7228.83,7231.89,7224.25,7292.84,7185.72,7230.67,7307.89,7227.5,0.08,0.39,0.1,0.17,0.16,0.0,0.2,0.13,1.0,0.36,0.23,0.97,0.0,0.19,0.56,0.05,1.89,0.32,0.76,0.72,7185.72,7310.45,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.02,0.0,0.01,0.01,7228.83,DLinear
+2024-02-06,7224.19,7245.77,7203.25,7200.75,7215.03,7174.85,7206.81,7257.77,7300.49,7369.7,7214.12,7210.87,7217.43,7212.0,7214.21,7214.99,7288.06,7159.48,7215.9,7300.92,7246.67,0.14,0.46,0.11,0.13,0.24,0.0,0.18,0.17,1.06,0.29,0.3,1.06,0.09,0.13,0.68,0.14,2.01,0.32,0.88,0.9,7159.48,7300.92,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.02,0.01,0.01,0.01,7245.77,Informer
+2024-02-07,7237.08,7241.63,7216.83,7221.2,7226.42,7222.58,7234.44,7277.5,7317.63,7377.89,7198.22,7229.34,7228.86,7227.31,7229.82,7221.54,7284.15,7172.75,7230.4,7282.88,7255.0,0.1,0.56,0.09,0.15,0.04,0.0,0.11,0.14,1.11,0.28,0.06,0.63,0.11,0.21,0.2,0.54,1.95,0.22,0.65,0.89,7172.75,7317.63,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.02,0.0,0.0,0.01,7241.63,Informer
+2024-02-08,7249.03,7225.22,7221.61,7236.65,7242.4,7235.89,7252.49,7285.7,7321.4,7382.84,7189.21,7239.06,7239.93,7231.63,7251.7,7236.11,7280.63,7193.89,7252.08,7254.66,7270.0,0.04,0.51,0.04,0.09,0.05,0.0,0.14,0.24,1.0,0.38,0.33,0.08,0.13,0.18,0.18,0.83,1.85,0.17,0.44,0.76,7189.21,7321.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.02,0.0,0.0,0.01,7280.63,StemGNN
+2024-02-18,7257.58,7232.66,7232.76,7247.51,7258.08,7252.65,7269.86,7295.25,7334.03,7388.26,7192.33,7253.86,7251.03,7230.31,7268.39,7263.02,7278.08,7215.18,7272.44,7244.53,7336.67,0.15,0.52,0.2,0.01,0.17,0.0,0.05,0.38,1.05,0.34,0.34,0.18,0.09,0.07,0.07,0.9,1.8,0.14,0.28,0.58,7192.33,7334.03,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.01,0.01,0.01,0.02,7334.03,GRU
+2024-02-19,7329.8,7284.9,7279.87,7271.41,7312.25,7328.12,7333.28,7359.55,7391.84,7412.85,7285.85,7321.05,7295.19,7280.82,7344.06,7349.32,7277.16,7268.04,7323.31,7273.12,7350.0,0.19,0.41,0.09,0.24,0.05,0.0,0.12,0.67,0.85,0.68,0.61,0.77,0.47,0.27,0.02,0.6,1.13,0.8,0.72,0.84,7268.04,7412.85,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.0,0.01,0.01,0.01,0.01,0.01,7349.32,TFT
+2024-02-20,7343.01,7301.36,7287.02,7283.48,7338.11,7398.41,7344.57,7358.8,7393.28,7424.57,7353.08,7341.83,7322.19,7304.78,7354.74,7371.01,7276.7,7309.68,7334.03,7338.48,7335.0,0.16,0.21,0.12,0.07,0.02,0.0,0.02,0.52,0.68,0.76,0.57,0.06,0.28,0.38,0.75,0.14,1.11,0.81,0.9,0.45,7276.7,7424.57,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.0,7334.03,TiDE
+2024-02-21,7331.02,7289.8,7277.52,7284.16,7334.13,7417.16,7333.0,7344.13,7379.12,7429.64,7381.22,7344.5,7320.66,7332.96,7337.17,7360.45,7275.86,7320.94,7325.44,7337.84,7297.5,0.08,0.18,0.08,0.04,0.03,0.0,0.18,0.03,0.66,0.73,0.56,0.09,0.14,0.4,1.18,0.68,1.35,0.64,0.75,0.14,7275.86,7429.64,0.01,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.02,0.01,0.02,0.0,0.0,0.0,7289.8,Informer
+2024-02-22,7286.55,7266.5,7249.85,7285.34,7304.14,7392.3,7305.8,7314.63,7345.37,7420.07,7373.2,7311.53,7302.57,7318.15,7298.16,7284.19,7273.93,7304.0,7292.32,7342.6,7285.0,0.16,0.39,0.08,0.24,0.26,0.0,0.34,0.43,0.81,0.5,0.28,0.77,0.22,0.03,1.45,1.19,1.83,0.02,0.17,0.24,7249.85,7392.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.02,0.0,0.0,0.0,7285.34,iTransformer
+2024-02-23,7272.61,7258.89,7241.55,7287.63,7288.24,7365.99,7296.96,7295.77,7340.09,7413.29,7359.23,7296.67,7292.49,7303.51,7281.81,7269.99,7271.49,7289.99,7281.0,7323.98,7306.67,0.13,0.32,0.12,0.21,0.33,0.0,0.33,0.42,0.93,0.43,0.19,0.71,0.27,0.04,1.28,1.19,1.93,0.21,0.02,0.24,7241.55,7365.99,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.0,0.0,7303.51,DLinear
+2024-02-26,7318.19,7258.93,7254.44,7295.47,7299.03,7362.35,7321.52,7317.68,7358.98,7413.28,7357.37,7301.95,7320.65,7296.1,7301.41,7307.13,7269.8,7306.61,7295.1,7371.11,7292.5,0.23,0.01,0.32,0.26,0.05,0.0,0.22,0.3,0.56,0.87,0.81,0.72,0.03,0.15,0.6,0.54,1.3,0.31,0.66,0.16,7254.44,7413.28,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.0,0.01,0.01,0.02,0.0,0.0,0.0,7295.1,TiDE
+2024-02-27,7324.57,7251.97,7242.58,7290.08,7297.98,7317.99,7297.46,7304.28,7342.81,7408.67,7340.79,7289.78,7327.02,7292.81,7282.36,7282.91,7268.05,7299.63,7286.47,7368.65,7306.67,0.58,0.28,0.52,0.36,0.37,0.0,0.47,0.43,0.25,1.12,0.99,0.6,0.03,0.57,0.09,0.22,1.15,0.47,0.77,0.34,7242.58,7408.67,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,7304.28,RNN
+2024-02-28,7352.75,7295.49,7254.83,7276.21,7302.82,7348.5,7323.02,7324.8,7359.94,7413.98,7330.58,7305.92,7341.13,7310.9,7305.61,7301.83,7266.96,7303.46,7303.91,7380.02,7353.33,0.64,0.38,0.66,0.68,0.4,0.0,0.64,0.57,0.1,1.33,0.78,0.37,0.16,0.69,0.06,0.3,0.83,1.04,1.17,0.67,7254.83,7413.98,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.01,7352.75,NHITS
+2024-02-29,7365.17,7313.22,7288.96,7293.72,7334.31,7378.47,7368.02,7371.75,7401.12,7425.51,7341.83,7336.51,7371.22,7333.33,7360.72,7354.87,7266.78,7327.83,7343.33,7374.52,7325.0,0.06,0.09,0.3,0.42,0.04,0.0,0.39,0.43,0.49,1.03,0.71,0.13,0.08,0.14,0.18,0.32,0.82,0.97,1.34,0.51,7266.78,7425.51,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.0,7327.83,MLPMultivariate
+2024-03-01,7320.5,7306.35,7263.73,7277.35,7322.0,7343.2,7329.76,7327.44,7364.73,7416.31,7322.68,7306.52,7337.22,7315.45,7335.42,7314.74,7266.41,7308.63,7327.13,7294.14,7343.33,0.2,0.09,0.09,0.02,0.13,0.0,0.19,0.07,0.6,0.78,0.19,0.36,0.23,0.08,0.31,0.03,1.31,0.59,0.74,0.16,7263.73,7416.31,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7343.2,TSMixerx
+2024-03-04,7334.56,7330.43,7283.3,7276.89,7330.63,7344.31,7334.27,7357.22,7391.92,7422.3,7315.67,7328.23,7328.13,7333.05,7339.2,7314.98,7266.39,7306.47,7342.18,7296.81,7330.0,0.06,0.31,0.1,0.05,0.0,0.0,0.09,0.02,0.78,0.7,0.06,0.51,0.09,0.27,0.13,0.26,1.2,0.79,0.93,0.38,7266.39,7422.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7330.43,Informer
+2024-03-05,7309.45,7325.04,7267.88,7277.67,7326.97,7335.79,7319.16,7340.15,7370.5,7418.13,7305.37,7318.76,7320.53,7326.05,7321.28,7293.51,7266.15,7303.62,7328.04,7281.27,7310.0,0.16,0.42,0.25,0.24,0.13,0.0,0.13,0.23,0.84,0.57,0.21,0.39,0.15,0.22,0.36,0.06,1.49,0.43,0.59,0.08,7266.15,7418.13,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,7309.45,NHITS
+2024-03-06,7304.78,7318.4,7255.75,7276.86,7305.71,7311.24,7313.83,7311.53,7357.06,7412.45,7301.94,7304.3,7315.76,7309.79,7297.15,7279.6,7265.64,7295.52,7301.35,7288.95,7297.5,0.1,0.09,0.05,0.01,0.12,0.0,0.01,0.07,0.72,0.67,0.19,0.22,0.15,0.34,0.09,0.04,1.47,0.38,0.54,0.13,7255.75,7412.45,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.0,7297.15,NLinear
+2024-03-07,7302.21,7321.25,7246.71,7271.87,7294.17,7309.87,7308.66,7314.28,7347.75,7407.47,7294.43,7290.02,7319.87,7285.87,7297.02,7274.12,7264.97,7290.85,7293.32,7295.53,7290.0,0.07,0.17,0.12,0.11,0.09,0.0,0.17,0.22,0.62,0.76,0.26,0.09,0.24,0.38,0.1,0.11,1.44,0.42,0.51,0.16,7246.71,7407.47,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.0,7290.02,DilatedRNN
+2024-03-08,7292.95,7299.1,7241.47,7246.3,7288.79,7302.01,7299.54,7302.63,7343.0,7403.52,7279.52,7288.72,7290.22,7284.58,7288.43,7271.91,7264.57,7279.09,7290.29,7294.8,7295.0,0.06,0.13,0.04,0.06,0.09,0.0,0.06,0.11,0.69,0.71,0.08,0.03,0.04,0.29,0.12,0.18,1.52,0.64,0.39,0.19,7241.47,7343.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,7294.8,DeepNPTS
+2024-03-11,7283.5,7308.73,7245.06,7233.56,7289.62,7272.81,7302.29,7308.69,7348.58,7402.53,7275.18,7290.97,7265.94,7297.8,7293.46,7274.37,7264.62,7276.64,7289.43,7294.81,7285.0,0.14,0.35,0.08,0.08,0.26,0.0,0.1,0.2,0.89,0.53,0.35,0.16,0.24,0.13,0.15,0.11,1.63,0.69,0.26,0.09,7233.56,7348.58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.0,0.0,7283.5,NHITS
+2024-03-12,7264.78,7291.06,7238.1,7239.95,7277.58,7269.54,7282.67,7303.06,7339.16,7395.51,7277.12,7280.3,7244.14,7296.69,7275.29,7269.44,7265.88,7271.18,7270.28,7285.48,7283.33,0.14,0.53,0.08,0.18,0.25,0.0,0.21,0.44,1.02,0.37,0.36,0.28,0.28,0.06,0.07,0.17,1.8,0.34,0.02,0.09,7238.1,7339.16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.01,0.0,0.0,7282.67,PatchTST
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+2024-04-09,7508.91,7451.4,7454.91,7495.67,7458.67,7404.45,7504.04,7508.22,7535.45,7498.07,7362.18,7500.56,7456.07,7436.46,7490.4,7479.82,7307.45,7470.89,7473.05,7482.98,7507.5,0.25,0.01,0.48,0.67,0.06,0.0,0.11,0.96,0.35,0.72,0.77,0.35,0.7,0.39,1.39,1.95,0.14,0.18,2.68,0.51,7404.45,7535.45,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.01,0.0,0.01,0.02,0.0,0.0,0.03,0.0,7508.22,RNN
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+2024-04-11,7504.87,7435.27,7455.34,7541.44,7475.08,7402.14,7456.31,7507.48,7525.09,7529.47,7398.46,7516.28,7430.02,7497.04,7500.72,7442.53,7317.92,7486.73,7485.56,7459.59,7497.5,0.06,0.03,0.26,0.4,0.65,0.0,0.15,0.1,0.27,0.66,0.93,0.6,1.0,0.83,1.37,1.42,0.33,0.49,2.49,0.24,7398.46,7541.44,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.02,0.0,7497.04,DLinear
+2024-04-12,7479.46,7435.88,7455.09,7449.45,7465.52,7413.95,7465.76,7506.2,7527.13,7527.37,7413.39,7518.23,7412.17,7521.26,7487.64,7408.6,7322.92,7468.34,7477.26,7416.74,7506.67,0.11,0.36,0.03,0.19,0.18,0.0,0.52,0.56,0.64,0.33,0.58,0.84,0.9,0.95,0.88,0.88,0.64,0.4,2.09,0.15,7408.6,7527.37,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.02,0.01,7506.2,RNN
+2024-04-15,7472.32,7395.35,7463.68,7434.46,7464.76,7388.03,7470.02,7506.19,7537.05,7531.6,7428.35,7524.9,7414.0,7503.56,7498.12,7387.92,7327.57,7459.92,7479.79,7412.79,7482.5,0.35,0.45,0.1,0.1,0.03,0.0,0.7,0.42,0.87,0.12,1.03,0.8,0.78,1.13,1.13,0.59,0.79,0.51,1.94,0.17,7387.92,7537.05,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.0,7479.79,TiDE
+2024-04-16,7445.28,7384.64,7424.47,7387.4,7448.83,7379.28,7438.86,7474.01,7498.57,7514.49,7421.47,7486.23,7387.0,7473.29,7463.03,7381.95,7330.04,7305.22,7465.68,7344.09,7495.0,0.24,0.39,0.27,0.05,0.09,0.0,0.55,0.38,0.72,0.28,0.81,1.36,0.78,0.85,0.89,0.32,0.93,0.78,1.55,1.88,7344.09,7514.49,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.01,0.02,0.01,0.02,0.02,0.01,0.0,0.01,0.02,0.03,7498.57,GRU
+2024-04-17,7446.2,7378.36,7448.03,7381.32,7454.08,7369.86,7440.14,7500.86,7526.87,7516.48,7420.73,7502.13,7377.72,7467.53,7480.13,7386.93,7332.3,7268.14,7470.71,7356.98,7497.5,0.46,0.73,0.33,0.11,0.08,0.0,0.75,0.29,1.08,0.02,0.91,1.2,0.92,0.8,1.03,0.34,0.94,0.87,1.53,2.39,7356.98,7526.87,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.02,0.02,0.02,0.01,0.02,0.01,0.0,0.02,0.02,0.03,7500.86,RNN
+2024-04-18,7457.83,7363.61,7444.84,7383.72,7460.04,7373.79,7419.24,7500.61,7521.25,7508.95,7418.34,7495.19,7396.12,7451.62,7494.75,7389.4,7335.15,7228.79,7474.29,7391.17,7497.5,0.5,0.57,0.22,0.03,0.52,0.0,0.5,0.08,0.85,0.17,1.26,0.89,0.83,0.92,1.13,0.53,0.69,0.99,1.64,3.07,7363.61,7521.25,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.01,0.02,0.01,0.01,0.01,0.02,0.01,0.0,0.02,0.02,0.04,7495.19,DilatedRNN
+2024-04-19,7469.48,7391.86,7446.51,7398.17,7463.59,7377.7,7399.12,7494.52,7524.0,7505.1,7416.04,7489.72,7404.43,7461.7,7489.8,7391.95,7339.46,7183.49,7469.89,7418.57,7527.5,0.27,0.34,0.01,0.08,0.94,0.0,0.27,0.1,0.73,0.31,1.04,0.68,0.87,1.04,1.23,0.72,0.48,0.95,1.74,3.83,7377.7,7524.0,0.01,0.0,0.01,0.01,0.02,0.01,0.01,0.01,0.0,0.01,0.02,0.01,0.02,0.02,0.02,0.01,0.0,0.02,0.02,0.05,7524.0,GRU
+2024-04-22,7496.98,7406.58,7488.83,7424.42,7488.17,7396.38,7437.13,7540.59,7567.34,7522.83,7419.43,7523.84,7447.05,7465.61,7533.4,7406.44,7344.58,7230.71,7495.75,7421.88,7527.5,0.49,0.58,0.02,0.12,0.8,0.0,0.36,0.42,0.94,0.11,1.21,1.0,0.67,1.21,1.34,1.03,0.34,0.97,2.03,3.55,7396.38,7567.34,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.01,0.02,0.01,0.01,0.02,0.02,0.01,0.0,0.01,0.02,0.04,7523.84,DilatedRNN
+2024-04-23,7500.82,7452.72,7481.53,7395.13,7502.22,7423.83,7450.05,7529.01,7554.42,7525.62,7422.18,7519.67,7474.19,7493.49,7518.56,7409.24,7349.08,7271.15,7508.02,7407.19,7510.0,0.24,0.38,0.1,0.02,0.68,0.0,0.25,0.1,0.71,0.26,0.64,1.25,0.35,1.22,1.03,1.05,0.33,1.41,2.02,3.06,7395.13,7554.42,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.02,0.02,0.03,7508.02,TiDE
+2024-04-24,7497.27,7500.77,7459.8,7455.1,7496.63,7431.4,7476.97,7509.26,7532.07,7519.69,7425.21,7500.56,7480.36,7501.79,7492.2,7399.97,7354.25,7295.49,7501.93,7410.32,7487.5,0.07,0.16,0.06,0.01,0.27,0.0,0.04,0.06,0.46,0.5,0.05,1.16,0.23,1.3,0.88,0.96,0.3,0.56,1.91,2.69,7399.97,7532.07,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.0,0.0,0.02,0.03,7492.2,NLinear
+2024-04-25,7494.2,7521.97,7429.77,7471.33,7478.92,7441.51,7488.24,7489.95,7505.15,7494.85,7433.35,7474.95,7478.76,7484.28,7477.99,7392.8,7358.79,7279.72,7481.18,7420.67,7487.5,0.22,0.06,0.17,0.2,0.08,0.0,0.26,0.13,0.15,0.86,0.37,0.98,0.21,1.35,0.7,0.81,0.01,0.31,1.81,2.86,7392.8,7521.97,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.0,0.0,0.02,0.03,7488.24,PatchTST
+2024-04-26,7487.55,7493.94,7433.02,7460.56,7472.8,7448.11,7513.83,7482.12,7512.5,7489.62,7445.96,7481.97,7489.84,7486.69,7476.71,7395.86,7361.77,7288.1,7477.9,7450.74,7502.5,0.14,0.07,0.13,0.2,0.35,0.0,0.07,0.01,0.33,0.73,0.09,0.49,0.03,1.22,0.53,0.56,0.03,0.36,1.68,2.66,7395.86,7513.83,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.0,0.01,0.02,0.03,7493.94,Informer
+2024-04-28,7492.31,7527.77,7453.0,7454.06,7481.64,7470.31,7526.84,7501.31,7531.63,7497.66,7466.42,7499.28,7485.05,7472.34,7496.09,7403.69,7365.99,7287.76,7486.34,7491.89,7527.5,0.05,0.12,0.08,0.14,0.46,0.0,0.09,0.27,0.52,0.52,0.47,0.01,0.1,1.18,0.29,0.35,0.07,0.51,1.69,2.73,7403.69,7531.63,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.02,0.01,0.01,0.0,0.01,0.02,0.03,7527.77,Informer
+2024-04-29,7498.57,7548.47,7486.26,7431.35,7495.67,7531.22,7528.19,7533.16,7564.23,7521.28,7480.11,7535.52,7457.69,7502.54,7512.45,7415.22,7369.91,7304.96,7503.48,7512.67,7537.5,0.19,0.46,0.07,0.04,0.4,0.0,0.49,0.05,0.88,0.16,0.67,0.19,0.55,1.11,0.44,0.25,0.3,0.9,1.72,2.58,7415.22,7564.23,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.02,0.0,0.01,0.0,0.01,0.02,0.03,7535.52,DilatedRNN
+2024-04-30,7495.75,7592.61,7496.31,7370.04,7501.83,7521.1,7525.03,7545.76,7570.16,7542.07,7482.28,7551.96,7438.24,7535.52,7529.03,7422.59,7374.53,7279.18,7509.82,7520.88,7532.5,0.44,0.67,0.19,0.08,0.39,0.0,0.75,0.53,0.99,0.01,1.29,0.34,0.77,0.98,0.34,0.18,0.62,1.68,1.62,2.89,7422.59,7592.61,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.01,0.0,0.02,0.02,0.03,7535.52,DLinear
+2024-05-06,7486.98,7575.81,7488.82,7356.33,7496.14,7475.81,7510.93,7539.36,7560.82,7539.02,7472.66,7538.92,7425.33,7523.91,7531.51,7425.09,7380.07,7292.73,7510.87,7504.35,7502.5,0.59,0.7,0.32,0.12,0.32,0.0,0.69,0.49,0.99,0.02,1.19,0.23,0.82,0.83,0.15,0.19,0.7,1.75,1.43,2.59,7380.07,7575.81,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.0,0.02,0.02,0.03,7504.35,DeepNPTS
+2024-05-07,7459.47,7522.92,7446.9,7293.06,7472.45,7416.02,7473.61,7500.68,7517.77,7513.82,7461.0,7495.62,7401.71,7499.71,7493.95,7423.25,7385.97,7269.02,7482.75,7427.8,7475.0,0.46,0.55,0.31,0.17,0.19,0.0,0.48,0.54,0.78,0.17,0.85,0.42,0.77,0.49,0.58,0.02,0.73,2.23,0.99,2.55,7385.97,7522.92,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.02,0.01,0.03,7473.61,PatchTST
+2024-05-08,7452.1,7498.92,7412.88,7266.95,7451.0,7393.7,7456.99,7466.12,7491.15,7492.59,7451.26,7466.67,7400.62,7459.54,7476.41,7422.86,7391.94,7251.08,7457.2,7413.32,7512.5,0.33,0.19,0.07,0.01,0.07,0.0,0.2,0.1,0.52,0.53,0.63,0.52,0.69,0.39,0.78,0.01,0.54,2.48,0.81,2.7,7391.94,7498.92,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.02,0.01,0.0,0.03,0.02,0.03,7498.92,Informer
+2024-05-09,7507.17,7481.83,7470.25,7276.28,7474.45,7402.05,7487.83,7516.03,7551.66,7514.2,7457.1,7515.92,7427.3,7461.2,7499.92,7434.42,7398.46,7258.22,7479.38,7489.75,7505.0,0.1,0.12,0.37,0.44,0.26,0.0,0.12,0.61,0.59,0.49,0.34,0.23,1.06,0.97,1.4,0.67,0.09,3.08,1.45,3.32,7398.46,7551.66,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.03,0.01,0.03,7507.17,NHITS
+2024-05-10,7513.2,7490.87,7449.28,7300.66,7485.6,7421.4,7469.69,7498.91,7523.2,7512.77,7449.59,7501.96,7431.21,7468.93,7501.23,7437.45,7405.6,7252.87,7475.75,7494.29,7477.5,0.16,0.19,0.5,0.37,0.58,0.0,0.15,0.59,0.13,0.85,0.3,0.25,1.09,1.01,1.22,0.85,0.01,2.83,1.43,3.46,7405.6,7523.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.02,0.01,0.03,7475.75,TiDE
+2024-05-11,7483.99,7472.65,7416.67,7318.22,7474.62,7444.24,7451.12,7474.21,7495.17,7500.56,7435.79,7469.77,7434.19,7468.85,7475.62,7438.88,7412.61,7258.36,7463.97,7463.24,7475.0,0.11,0.13,0.27,0.13,0.44,0.0,0.19,0.2,0.15,0.9,0.15,0.28,0.67,0.6,0.53,0.64,0.22,2.21,0.95,3.01,7412.61,7500.56,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.02,0.01,0.03,7474.62,TSMixer
+2024-05-13,7492.02,7469.96,7417.52,7332.23,7473.29,7459.2,7460.44,7485.61,7499.11,7486.36,7435.56,7462.95,7449.81,7487.18,7477.07,7448.36,7419.8,7278.9,7470.81,7462.39,7485.0,0.2,0.09,0.28,0.25,0.42,0.0,0.39,0.06,0.09,0.99,0.29,0.4,0.56,0.58,0.44,0.75,0.08,2.13,0.96,2.84,7417.52,7499.11,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.02,0.01,0.03,7485.61,RNN
+2024-05-14,7485.84,7471.29,7430.62,7342.64,7479.87,7489.41,7462.96,7479.66,7511.71,7483.63,7457.97,7468.02,7482.82,7489.58,7495.8,7450.75,7428.02,7325.6,7475.31,7470.97,7507.5,0.13,0.08,0.14,0.08,0.31,0.0,0.24,0.05,0.35,0.74,0.19,0.2,0.04,0.47,0.05,0.37,0.03,1.91,0.77,2.14,7428.02,7511.71,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.02,0.01,0.02,7511.71,GRU
+2024-05-15,7494.66,7527.76,7462.12,7357.79,7498.05,7496.8,7487.66,7508.05,7541.39,7503.29,7486.74,7493.37,7518.69,7500.46,7501.49,7471.96,7432.85,7329.11,7495.48,7474.04,7510.0,0.09,0.18,0.01,0.05,0.09,0.0,0.02,0.08,0.62,0.43,0.44,0.28,0.32,0.3,0.03,0.11,0.12,1.83,0.82,2.21,7432.85,7541.39,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.01,0.02,7508.05,RNN
+2024-05-16,7486.32,7547.16,7461.93,7375.92,7505.98,7500.59,7506.94,7517.17,7536.9,7516.66,7504.73,7500.54,7524.96,7515.96,7497.25,7475.93,7436.31,7369.26,7509.39,7487.81,7525.0,0.15,0.41,0.31,0.26,0.28,0.0,0.19,0.4,0.68,0.33,0.81,0.02,0.52,0.14,0.19,0.25,0.41,1.47,0.67,1.56,7375.92,7547.16,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.01,0.02,7524.96,MLP
+2024-05-17,7495.27,7547.66,7484.75,7363.66,7512.85,7512.16,7527.19,7534.39,7561.08,7537.95,7517.79,7529.81,7511.87,7526.4,7519.39,7487.89,7438.31,7337.0,7520.01,7495.98,7587.5,0.32,0.52,0.33,0.23,0.43,0.0,0.46,0.42,0.88,0.14,0.7,0.01,0.22,0.1,0.23,0.3,0.57,1.76,0.76,2.11,7438.31,7561.08,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.03,0.02,0.03,7561.08,GRU
+2024-05-20,7560.91,7627.52,7571.38,7382.25,7551.36,7540.14,7599.44,7615.53,7644.78,7640.54,7541.9,7609.56,7537.17,7538.88,7591.33,7608.87,7440.06,7344.79,7572.66,7580.6,7670.0,0.4,0.72,0.16,0.13,0.51,0.0,0.64,0.29,1.11,0.14,0.88,0.26,0.31,0.63,0.27,0.25,1.05,2.36,1.6,2.86,7537.17,7644.78,0.01,0.01,0.01,0.02,0.01,0.01,0.01,0.02,0.0,0.01,0.01,0.01,0.02,0.01,0.02,0.02,0.0,0.04,0.03,0.04,7644.78,GRU
+2024-05-21,7658.46,7702.18,7684.89,7412.29,7615.56,7572.75,7679.05,7723.06,7740.26,7767.59,7554.82,7719.04,7600.38,7599.58,7671.84,7656.84,7442.83,7387.49,7645.65,7667.59,7677.5,0.17,0.84,0.17,0.56,0.27,0.0,0.79,0.77,1.07,0.35,0.57,0.12,0.76,0.02,1.12,1.35,1.42,3.21,2.82,3.54,7554.82,7767.59,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.02,0.01,0.03,0.03,0.04,7679.05,PatchTST
+2024-05-22,7658.66,7694.97,7674.66,7429.14,7638.08,7568.24,7632.54,7712.51,7717.9,7771.7,7542.16,7724.91,7588.67,7629.96,7679.33,7656.76,7446.85,7412.06,7659.85,7672.34,7677.5,0.27,0.7,0.02,0.27,0.34,0.0,0.86,0.37,0.77,0.21,0.47,0.18,0.91,0.02,1.18,1.52,1.48,3.0,2.77,3.22,7568.24,7771.7,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.02,0.01,0.03,0.03,0.03,7679.33,NLinear
+2024-05-23,7642.4,7676.73,7676.06,7435.77,7634.23,7552.03,7612.69,7712.66,7718.55,7767.44,7536.28,7722.24,7566.67,7651.84,7671.41,7581.26,7452.62,7439.62,7656.34,7653.24,7645.0,0.38,0.92,0.18,0.11,0.39,0.0,1.04,0.12,1.0,0.44,0.45,0.14,0.99,0.8,1.18,1.39,1.64,2.7,2.48,2.65,7536.28,7722.24,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.02,0.03,0.03,0.03,7642.4,NHITS
+2024-05-24,7609.85,7677.56,7617.06,7413.75,7605.9,7513.65,7579.41,7654.46,7660.86,7738.27,7534.54,7674.64,7539.22,7650.42,7640.53,7529.25,7459.19,7459.08,7620.13,7598.74,7630.0,0.4,0.59,0.14,0.05,0.4,0.0,0.85,0.53,0.67,0.09,0.89,0.15,0.93,1.06,1.26,0.99,1.69,2.58,1.98,1.98,7513.65,7677.56,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.02,0.01,0.01,0.03,0.02,0.02,7620.13,TiDE
+2024-05-27,7602.61,7604.74,7602.11,7442.68,7591.02,7509.58,7585.75,7619.81,7655.25,7679.82,7535.6,7648.6,7529.26,7610.52,7622.08,7536.4,7467.0,7448.39,7606.81,7607.49,7650.0,0.26,0.23,0.06,0.15,0.22,0.0,0.6,0.1,0.69,0.01,0.03,0.06,0.96,0.87,1.22,0.88,1.02,2.1,1.78,2.03,7509.58,7679.82,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.02,0.01,0.02,0.01,0.0,0.03,0.02,0.03,7648.6,DilatedRNN
+2024-05-28,7621.53,7579.11,7632.67,7460.22,7604.76,7536.8,7604.37,7666.65,7688.61,7713.19,7536.61,7676.49,7535.3,7602.96,7628.69,7561.36,7475.23,7472.05,7623.2,7637.36,7690.0,0.09,0.59,0.02,0.22,0.23,0.0,0.72,0.24,0.88,0.15,0.56,0.21,1.13,0.79,1.11,1.11,1.2,2.12,1.92,1.96,7535.3,7713.19,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.02,0.02,0.02,0.02,0.0,0.03,0.03,0.03,7688.61,GRU
+2024-05-29,7660.31,7564.95,7690.2,7542.13,7642.38,7556.52,7635.98,7722.5,7740.62,7758.82,7553.88,7726.35,7564.89,7633.76,7662.29,7584.71,7483.88,7537.41,7655.15,7643.97,7690.0,0.03,0.81,0.07,0.23,0.32,0.0,0.86,0.35,1.05,0.39,1.24,0.21,1.25,0.99,1.35,1.39,1.29,1.54,2.3,1.6,7553.88,7758.82,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.02,0.01,0.02,0.01,0.02,0.02,0.01,0.02,0.03,0.02,7690.2,LSTM
+2024-05-30,7659.5,7553.99,7679.95,7592.99,7652.8,7541.2,7625.44,7716.96,7723.41,7779.81,7572.33,7718.33,7566.44,7647.5,7675.07,7605.57,7492.21,7596.18,7666.5,7566.29,7716.67,0.2,0.75,0.09,0.09,0.44,0.0,0.77,0.16,0.83,0.27,1.38,1.22,1.21,0.7,1.54,1.14,1.57,0.87,2.18,0.83,7553.99,7723.41,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.02,0.02,0.02,0.01,0.02,0.02,0.01,0.02,0.03,0.02,7716.96,RNN
+2024-05-31,7676.04,7592.94,7721.02,7620.88,7669.84,7558.63,7640.45,7755.05,7767.45,7825.41,7589.97,7745.55,7604.26,7679.07,7714.9,7641.48,7499.87,7620.5,7694.39,7561.95,7740.0,0.51,1.03,0.24,0.08,0.46,0.0,0.91,0.04,1.19,0.59,1.08,1.49,0.94,0.45,1.53,1.12,1.95,0.72,2.29,0.72,7561.95,7767.45,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.02,0.02,0.02,0.01,0.02,0.02,0.01,0.02,0.03,0.02,7745.55,DilatedRNN
+2024-06-03,7690.19,7602.5,7744.59,7643.27,7692.21,7576.14,7643.83,7774.9,7783.75,7838.53,7598.89,7768.8,7652.97,7688.61,7744.76,7658.11,7507.05,7660.01,7713.29,7635.55,7720.0,0.71,1.1,0.3,0.03,0.6,0.0,1.02,0.02,1.22,0.71,1.14,0.71,0.48,0.42,1.48,1.19,1.93,0.61,2.38,0.39,7576.14,7783.75,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.02,0.01,0.01,0.01,0.02,0.02,0.02,0.01,0.03,0.01,7713.29,TiDE
+2024-06-04,7676.58,7604.72,7709.75,7625.74,7686.42,7586.88,7633.54,7732.22,7745.49,7822.4,7601.66,7739.83,7638.93,7670.32,7704.17,7640.85,7512.9,7624.79,7702.71,7650.31,7650.0,0.36,0.72,0.34,0.13,0.56,0.0,0.82,0.08,0.9,0.43,0.94,0.34,0.49,0.47,1.17,0.98,1.9,0.66,2.13,0.67,7586.88,7745.49,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.01,0.02,0.0,0.02,0.0,7650.31,DeepNPTS
+2024-06-05,7617.42,7585.56,7608.16,7521.1,7636.97,7592.04,7619.76,7642.54,7645.94,7710.69,7592.1,7655.94,7600.55,7656.55,7622.66,7582.8,7516.58,7580.1,7640.7,7558.49,7620.0,0.07,0.33,0.31,0.26,0.03,0.0,0.51,0.51,0.37,0.12,0.42,0.77,0.22,0.45,0.33,0.33,1.22,1.26,1.32,0.49,7516.58,7710.69,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,7619.76,PatchTST
+2024-06-06,7627.55,7601.65,7581.97,7498.69,7602.71,7600.4,7617.53,7613.13,7635.79,7570.58,7579.57,7631.12,7591.91,7629.57,7600.04,7577.28,7519.33,7549.89,7594.44,7584.87,7600.0,0.36,0.19,0.43,0.33,0.13,0.0,0.05,0.03,0.11,0.6,0.34,0.56,0.47,0.66,0.36,0.63,0.75,1.69,1.42,1.02,7519.33,7635.79,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7600.04,NLinear
+2024-06-07,7620.12,7576.07,7555.8,7496.98,7587.86,7584.3,7616.1,7584.17,7614.55,7535.23,7568.33,7605.75,7595.99,7578.05,7587.0,7574.39,7522.1,7512.69,7578.68,7597.59,7606.67,0.43,0.47,0.54,0.42,0.05,0.0,0.19,0.55,0.07,0.84,0.58,0.3,0.32,0.6,0.47,0.68,1.11,1.62,1.29,1.41,7512.69,7620.12,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,7605.75,DilatedRNN
+2024-06-11,7615.42,7561.42,7572.2,7494.23,7592.89,7608.43,7623.98,7598.93,7636.73,7560.02,7573.55,7615.37,7596.81,7575.98,7597.5,7578.83,7525.05,7527.02,7589.47,7608.48,7620.0,0.24,0.22,0.34,0.3,0.11,0.0,0.0,0.52,0.28,0.57,0.71,0.09,0.24,0.48,0.09,0.55,0.73,1.59,1.19,1.16,7525.05,7636.73,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.02,0.01,0.01,7623.98,PatchTST
+2024-06-12,7611.62,7553.93,7591.31,7496.85,7606.43,7639.72,7624.18,7636.33,7654.1,7637.44,7589.62,7633.55,7569.36,7605.97,7609.05,7591.22,7527.96,7539.77,7601.45,7616.41,7673.33,0.03,0.32,0.13,0.07,0.17,0.0,0.29,0.07,0.56,0.27,0.76,0.06,0.56,0.27,0.37,0.29,0.34,1.51,1.1,0.94,7527.96,7654.1,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.02,0.01,0.01,0.01,0.0,0.01,0.0,0.02,0.02,0.02,7654.1,GRU
+2024-06-13,7655.61,7603.46,7674.73,7519.39,7641.07,7638.81,7672.45,7713.7,7731.49,7805.59,7621.73,7705.76,7603.03,7659.96,7673.02,7621.81,7532.56,7584.4,7645.12,7648.14,7690.0,0.23,0.76,0.14,0.19,0.22,0.0,0.66,0.06,0.99,0.25,0.68,0.1,0.69,0.44,0.22,0.44,1.96,1.78,1.61,0.93,7584.4,7731.49,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.02,0.02,0.01,7674.73,LSTM
+2024-06-14,7662.58,7670.16,7685.94,7575.84,7660.44,7647.96,7682.6,7725.29,7731.85,7828.61,7646.61,7720.59,7639.52,7679.22,7692.25,7660.54,7536.97,7625.65,7665.64,7660.87,7690.0,0.39,0.82,0.04,0.03,0.26,0.0,0.76,0.22,0.9,0.3,0.1,0.02,0.3,0.03,0.19,0.21,2.17,1.13,1.64,0.48,7575.84,7731.85,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.02,0.01,0.02,0.01,7692.25,NLinear
+2024-06-17,7671.29,7647.84,7684.84,7597.49,7665.59,7665.5,7691.83,7715.76,7728.39,7830.33,7664.51,7720.84,7657.3,7667.72,7690.86,7643.12,7541.43,7646.18,7679.22,7629.43,7656.67,0.26,0.58,0.1,0.07,0.27,0.0,0.65,0.05,0.74,0.18,0.31,0.55,0.18,0.37,0.08,0.09,2.07,0.96,1.69,0.33,7597.49,7728.39,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.02,0.0,7657.3,MLP
+2024-06-18,7654.17,7665.9,7629.33,7606.86,7644.06,7636.13,7666.63,7667.54,7672.72,7798.64,7652.07,7680.42,7619.72,7641.58,7656.95,7592.23,7545.62,7627.04,7657.63,7638.64,7656.67,0.04,0.17,0.05,0.13,0.16,0.0,0.34,0.16,0.24,0.32,0.15,0.2,0.45,0.81,0.24,0.03,1.89,0.62,1.42,0.35,7545.62,7680.42,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.02,0.01,0.01,0.0,7656.95,NLinear
+2024-06-19,7727.11,7640.08,7638.93,7578.59,7636.68,7591.02,7659.72,7664.19,7690.23,7756.19,7637.89,7681.55,7618.03,7634.64,7654.33,7591.34,7548.8,7635.99,7636.73,7637.2,7670.0,0.94,0.81,1.17,1.17,0.87,0.0,0.59,1.2,0.48,1.14,1.13,1.16,1.41,1.76,1.76,1.15,0.38,1.92,2.31,1.18,7618.03,7756.19,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.02,0.0,7664.19,RNN
+2024-06-20,7756.39,7659.25,7656.39,7564.65,7645.7,7595.62,7650.33,7687.96,7706.14,7773.77,7634.08,7701.54,7642.69,7644.28,7666.27,7593.11,7552.24,7671.8,7632.11,7650.3,7680.0,1.16,0.88,1.6,1.43,1.37,0.0,0.71,1.45,0.65,1.29,1.25,1.37,1.47,2.11,2.07,1.58,0.22,2.47,2.63,1.09,7642.69,7773.77,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.02,0.02,0.0,7687.96,RNN
+2024-06-21,7726.67,7685.7,7669.65,7563.31,7659.75,7610.74,7654.26,7698.64,7717.54,7814.91,7636.12,7712.27,7668.85,7668.87,7677.59,7596.45,7556.53,7678.47,7647.91,7660.17,7680.0,0.64,0.36,1.02,0.87,0.94,0.0,0.19,0.75,0.12,0.74,0.53,0.86,0.75,1.69,1.5,1.17,1.14,2.11,2.2,0.62,7636.12,7814.91,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.02,0.02,0.02,0.0,7678.47,MLPMultivariate
+2024-06-24,7694.64,7672.12,7667.38,7570.59,7668.28,7625.21,7646.98,7705.2,7713.55,7819.28,7629.2,7707.03,7645.71,7680.38,7684.28,7592.7,7561.85,7657.72,7666.29,7648.92,7680.0,0.13,0.14,0.37,0.34,0.62,0.0,0.16,0.19,0.25,0.35,0.29,0.59,0.64,1.32,0.9,0.85,1.62,1.61,1.73,0.48,7592.7,7713.55,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.02,0.01,0.02,0.0,7680.38,DLinear
+2024-06-25,7656.97,7685.36,7667.81,7584.83,7669.49,7652.55,7639.3,7702.86,7714.04,7804.54,7638.43,7698.53,7635.15,7695.28,7680.91,7592.76,7567.32,7651.89,7677.07,7634.02,7693.33,0.31,0.6,0.26,0.16,0.23,0.0,0.54,0.5,0.75,0.14,0.37,0.3,0.28,0.84,0.06,0.24,1.93,0.94,1.17,0.07,7567.32,7714.04,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.01,7695.28,DLinear
+2024-06-26,7663.05,7700.56,7688.21,7592.06,7675.82,7671.93,7645.04,7717.94,7733.98,7810.16,7655.06,7714.51,7645.3,7714.43,7695.17,7603.42,7572.47,7672.88,7691.75,7632.18,7716.67,0.42,0.72,0.37,0.17,0.23,0.0,0.67,0.67,0.93,0.33,0.49,0.4,0.23,0.78,0.12,0.1,1.92,0.93,1.18,0.13,7572.47,7733.98,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.02,0.01,7717.94,RNN
+2024-06-27,7677.4,7733.3,7717.34,7581.76,7688.27,7684.8,7672.33,7746.99,7762.31,7828.62,7679.04,7743.03,7653.37,7703.94,7708.81,7629.43,7577.01,7688.28,7699.69,7625.66,7723.33,0.41,0.91,0.29,0.14,0.07,0.0,0.85,0.35,1.11,0.52,0.73,0.67,0.31,0.62,0.1,0.02,1.97,1.25,1.31,0.14,7577.01,7762.31,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.02,0.02,0.0,7717.34,LSTM
+2024-06-28,7677.32,7757.11,7720.51,7600.88,7694.57,7678.53,7677.87,7750.27,7761.7,7842.71,7690.04,7751.73,7651.4,7685.27,7712.89,7630.63,7581.37,7685.2,7704.47,7616.63,7730.0,0.46,0.95,0.35,0.22,0.01,0.0,0.97,0.1,1.1,0.56,1.04,0.79,0.34,0.61,0.02,0.17,2.15,1.0,1.25,0.1,7581.37,7761.7,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.02,0.01,7720.51,LSTM
+2024-07-01,7707.93,7783.31,7729.01,7658.78,7698.83,7678.79,7708.14,7757.56,7769.41,7858.08,7695.38,7759.5,7666.04,7688.93,7720.96,7649.1,7586.13,7704.59,7715.52,7664.76,7763.33,0.17,0.64,0.1,0.12,0.0,0.0,0.67,0.25,0.8,0.27,0.98,0.56,0.54,0.76,0.38,0.16,1.95,0.64,1.58,0.04,7649.1,7783.31,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.01,7759.5,DilatedRNN
+2024-07-02,7751.55,7828.43,7773.13,7771.68,7722.98,7680.78,7739.17,7797.09,7808.54,7880.75,7688.06,7793.55,7695.02,7722.0,7756.72,7715.2,7592.57,7759.12,7744.77,7741.74,7780.0,0.07,0.59,0.09,0.37,0.16,0.0,0.54,0.38,0.74,0.28,0.99,0.13,0.73,0.47,0.91,0.82,1.67,0.26,2.05,0.1,7680.78,7828.43,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.0,0.02,0.0,7773.13,LSTM
+2024-07-03,7762.43,7821.1,7785.83,7768.14,7737.08,7688.67,7737.38,7805.72,7814.84,7884.52,7671.07,7805.74,7699.38,7736.65,7780.01,7781.53,7600.68,7781.87,7747.47,7776.13,7800.0,0.23,0.56,0.19,0.33,0.32,0.0,0.56,0.33,0.68,0.3,0.76,0.18,0.81,0.25,0.95,1.18,1.57,0.07,2.08,0.25,7671.07,7821.1,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.02,0.01,0.0,0.03,0.0,7805.72,RNN
+2024-07-04,7786.08,7820.99,7808.32,7778.61,7757.09,7698.67,7767.39,7826.63,7834.39,7886.31,7664.84,7824.24,7716.39,7755.69,7793.56,7744.54,7607.4,7810.21,7767.25,7795.52,7800.0,0.1,0.52,0.24,0.37,0.24,0.0,0.49,0.39,0.62,0.29,0.45,0.12,0.89,0.53,1.12,1.56,1.29,0.1,2.29,0.31,7698.67,7886.31,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.02,0.01,0.0,0.02,0.0,7795.52,DeepNPTS
+2024-07-05,7786.85,7820.6,7802.07,7857.05,7765.0,7704.08,7775.88,7824.34,7824.23,7867.37,7675.64,7822.93,7717.42,7771.03,7787.48,7722.59,7614.78,7784.03,7781.0,7773.13,7766.67,0.01,0.48,0.08,0.28,0.14,0.0,0.46,0.2,0.48,0.2,0.43,0.18,0.89,0.83,1.06,1.43,1.03,0.9,2.21,0.04,7675.64,7867.37,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.0,7765.0,TSMixer
+2024-07-08,7750.86,7775.85,7756.63,7790.97,7745.48,7728.45,7735.1,7771.43,7782.12,7830.41,7681.79,7783.1,7680.52,7766.86,7756.73,7673.49,7623.7,7743.8,7760.92,7733.4,7746.67,0.08,0.27,0.13,0.07,0.2,0.0,0.42,0.21,0.4,0.07,0.32,0.23,0.91,1.0,0.29,0.89,1.03,0.52,1.64,0.09,7673.49,7830.41,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.02,0.0,7745.48,TSMixer
+2024-07-09,7722.96,7771.44,7738.08,7700.89,7723.15,7740.89,7719.52,7758.3,7770.58,7808.82,7683.28,7764.36,7650.72,7738.94,7735.36,7674.87,7632.98,7718.29,7736.34,7708.61,7720.0,0.16,0.46,0.17,0.0,0.04,0.0,0.54,0.21,0.62,0.2,0.63,0.19,0.94,0.62,0.23,0.51,1.11,0.29,1.17,0.06,7632.98,7808.82,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.01,0.0,7719.52,PatchTST
+2024-07-10,7678.34,7688.29,7704.44,7589.11,7701.77,7712.37,7697.39,7727.91,7738.12,7775.17,7671.93,7736.38,7618.9,7709.76,7703.35,7668.79,7641.77,7677.77,7704.73,7692.53,7693.33,0.33,0.65,0.34,0.31,0.25,0.0,0.76,0.41,0.78,0.34,0.13,0.18,0.77,0.12,0.44,0.08,1.26,1.16,0.48,0.01,7589.11,7775.17,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7692.53,DeepNPTS
+2024-07-11,7648.69,7674.48,7673.89,7581.47,7676.21,7708.96,7680.71,7693.04,7711.88,7753.58,7666.8,7706.51,7619.0,7682.29,7677.71,7681.14,7649.18,7661.22,7672.21,7680.78,7686.67,0.38,0.58,0.31,0.36,0.42,0.0,0.76,0.44,0.83,0.33,0.34,0.42,0.39,0.42,0.79,0.24,1.37,0.88,0.01,0.16,7581.47,7753.58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.0,0.0,7682.29,DLinear
+2024-07-12,7669.61,7650.62,7671.48,7621.69,7665.51,7658.81,7675.2,7701.66,7714.63,7765.84,7667.84,7701.91,7626.23,7678.42,7672.11,7680.77,7656.73,7648.62,7658.77,7681.25,7680.0,0.03,0.42,0.14,0.05,0.07,0.0,0.42,0.11,0.59,0.02,0.25,0.15,0.57,0.15,0.14,0.02,1.25,0.62,0.17,0.27,7621.69,7765.84,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.0,0.0,7680.77,TFT
+2024-07-15,7674.74,7636.96,7662.9,7660.4,7664.41,7649.86,7662.4,7692.31,7707.55,7758.14,7666.8,7694.71,7642.93,7675.73,7670.65,7683.32,7663.66,7658.05,7658.65,7685.69,7680.0,0.05,0.23,0.21,0.13,0.16,0.0,0.26,0.01,0.43,0.15,0.49,0.14,0.41,0.11,0.32,0.1,1.09,0.19,0.14,0.22,7636.96,7758.14,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,7683.32,TFT
+2024-07-16,7651.22,7665.87,7666.36,7679.77,7669.12,7662.39,7662.32,7694.89,7712.67,7768.03,7660.25,7693.56,7646.65,7667.06,7679.83,7685.51,7667.3,7657.69,7669.74,7726.9,7666.67,0.37,0.57,0.24,0.23,0.15,0.0,0.55,0.21,0.8,0.2,0.19,0.99,0.06,0.45,0.15,0.12,1.53,0.37,0.21,0.08,7646.65,7726.9,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,7666.36,LSTM
+2024-07-17,7637.9,7660.13,7645.18,7660.46,7665.03,7655.32,7661.53,7680.92,7691.54,7757.11,7655.57,7673.35,7649.5,7651.11,7662.8,7670.56,7667.2,7655.92,7665.27,7701.17,7660.0,0.33,0.56,0.36,0.36,0.31,0.0,0.46,0.17,0.7,0.1,0.29,0.83,0.15,0.43,0.23,0.23,1.56,0.3,0.38,0.24,7637.9,7701.17,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,7660.13,Informer
+2024-07-18,7661.33,7666.53,7639.5,7683.98,7661.71,7662.09,7657.96,7670.44,7689.36,7759.37,7647.55,7671.03,7659.78,7652.43,7661.6,7657.73,7666.66,7654.79,7657.27,7689.07,7656.67,0.0,0.12,0.05,0.01,0.04,0.0,0.13,0.12,0.37,0.28,0.07,0.36,0.02,0.05,0.01,0.18,1.28,0.3,0.07,0.09,7639.5,7759.37,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,7657.27,TiDE
+2024-07-19,7665.03,7654.06,7635.24,7668.78,7662.77,7660.37,7654.84,7669.45,7686.07,7754.96,7643.93,7669.79,7665.58,7643.64,7663.61,7649.73,7666.01,7657.81,7657.13,7685.6,7656.67,0.02,0.06,0.1,0.03,0.13,0.0,0.06,0.28,0.27,0.39,0.14,0.27,0.01,0.2,0.06,0.28,1.17,0.05,0.01,0.09,7635.24,7754.96,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,7657.13,TiDE
+2024-07-22,7649.73,7644.27,7636.49,7644.6,7665.12,7666.42,7654.45,7668.51,7688.3,7764.49,7644.52,7672.99,7672.46,7658.74,7658.42,7642.28,7666.42,7664.4,7656.29,7667.09,7640.0,0.11,0.25,0.09,0.2,0.06,0.0,0.3,0.12,0.5,0.17,0.07,0.23,0.3,0.1,0.22,0.07,1.5,0.07,0.22,0.19,7636.49,7688.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.0,7642.28,TFT
+2024-07-23,7615.23,7645.56,7610.57,7655.94,7652.1,7662.34,7634.09,7645.28,7662.61,7733.56,7646.66,7650.71,7654.92,7659.24,7642.47,7624.17,7666.82,7659.42,7638.33,7656.65,7623.33,0.36,0.39,0.3,0.48,0.25,0.0,0.47,0.58,0.62,0.06,0.4,0.54,0.52,0.12,0.62,0.41,1.55,0.53,0.68,0.58,7610.57,7666.82,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,7624.17,TFT
+2024-07-24,7597.53,7650.06,7590.27,7719.11,7634.82,7637.72,7616.98,7624.78,7646.08,7661.27,7642.03,7625.71,7641.44,7656.11,7626.19,7609.78,7665.78,7651.82,7623.34,7656.74,7623.33,0.38,0.36,0.34,0.49,0.26,0.0,0.37,0.77,0.64,0.1,0.69,0.78,0.58,0.16,0.53,0.59,0.84,1.6,0.9,0.71,7590.27,7665.78,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,7623.34,TiDE
+2024-07-25,7608.65,7635.91,7593.96,7735.25,7627.76,7630.19,7616.05,7628.94,7653.01,7653.68,7631.24,7626.97,7640.3,7666.61,7627.16,7611.94,7663.56,7653.03,7621.39,7660.64,7616.67,0.24,0.27,0.17,0.25,0.1,0.0,0.24,0.76,0.58,0.19,0.36,0.68,0.42,0.04,0.28,0.3,0.59,1.66,0.72,0.58,7593.96,7666.61,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.0,7616.05,PatchTST
+2024-07-26,7617.22,7632.98,7583.26,7719.98,7622.01,7624.53,7601.33,7616.61,7641.8,7622.3,7619.09,7615.34,7640.72,7653.16,7615.57,7609.45,7661.1,7643.86,7611.62,7667.77,7606.67,0.02,0.01,0.07,0.06,0.21,0.0,0.02,0.47,0.32,0.45,0.21,0.66,0.31,0.1,0.1,0.02,0.07,1.35,0.58,0.35,7583.26,7719.98,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,7609.45,TFT
+2024-07-29,7632.45,7609.45,7571.18,7713.09,7611.68,7620.21,7594.82,7607.42,7631.3,7611.93,7608.13,7602.7,7638.04,7621.31,7607.55,7595.9,7657.72,7625.12,7607.82,7630.84,7593.33,0.33,0.33,0.32,0.27,0.49,0.0,0.39,0.15,0.02,0.8,0.3,0.02,0.07,0.48,0.16,0.32,0.27,1.06,0.33,0.1,7571.18,7713.09,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.02,0.01,0.0,7594.82,PatchTST
+2024-07-30,7621.05,7605.67,7554.07,7693.67,7599.03,7601.6,7588.37,7595.38,7615.72,7591.75,7594.35,7583.41,7629.58,7602.19,7598.93,7574.65,7653.51,7600.72,7596.26,7617.22,7593.33,0.29,0.34,0.33,0.29,0.43,0.0,0.49,0.25,0.07,0.88,0.2,0.05,0.11,0.61,0.26,0.35,0.38,0.95,0.43,0.27,7554.07,7693.67,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,7594.35,BiTCN
+2024-07-31,7587.46,7604.13,7557.67,7676.02,7593.74,7595.79,7589.72,7594.85,7621.85,7591.03,7581.07,7582.76,7605.31,7587.62,7602.61,7569.93,7649.69,7571.38,7593.87,7608.1,7583.33,0.2,0.1,0.08,0.08,0.03,0.0,0.06,0.0,0.45,0.39,0.22,0.27,0.24,0.23,0.11,0.08,0.05,1.17,0.82,0.21,7557.67,7676.02,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,7582.76,DilatedRNN
+2024-08-01,7576.43,7594.77,7542.38,7676.91,7583.79,7575.55,7576.5,7581.67,7606.06,7578.42,7566.83,7567.78,7578.68,7581.86,7588.04,7561.38,7647.13,7543.93,7580.26,7601.32,7620.0,0.15,0.07,0.05,0.1,0.0,0.0,0.11,0.07,0.39,0.45,0.24,0.33,0.03,0.2,0.01,0.13,0.03,1.33,0.93,0.43,7542.38,7676.91,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,7606.06,GRU
+2024-08-02,7616.25,7599.38,7601.53,7702.78,7607.2,7603.66,7609.55,7638.54,7668.72,7714.11,7574.54,7622.81,7587.85,7601.98,7625.29,7605.34,7647.13,7558.22,7606.16,7607.22,7623.33,0.12,0.29,0.13,0.12,0.09,0.0,0.09,0.19,0.69,0.19,0.22,0.12,0.37,0.14,0.17,0.55,1.28,1.14,0.41,0.76,7558.22,7714.11,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,7622.81,DilatedRNN
+2024-08-05,7621.53,7592.47,7595.34,7698.87,7621.36,7617.55,7611.03,7638.07,7653.23,7735.43,7591.9,7625.9,7595.08,7607.4,7626.78,7606.12,7648.05,7577.72,7617.05,7605.43,7610.0,0.07,0.22,0.06,0.0,0.14,0.0,0.06,0.19,0.42,0.34,0.38,0.21,0.35,0.2,0.05,0.39,1.49,1.01,0.35,0.57,7577.72,7735.43,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.0,0.0,7611.03,PatchTST
+2024-08-06,7614.82,7598.47,7578.08,7696.71,7616.64,7609.5,7607.33,7612.45,7636.6,7706.2,7602.73,7605.37,7605.46,7622.93,7609.92,7603.31,7648.18,7593.61,7621.72,7609.77,7596.67,0.06,0.03,0.09,0.02,0.1,0.0,0.12,0.11,0.29,0.48,0.21,0.07,0.12,0.15,0.07,0.16,1.2,1.08,0.44,0.28,7578.08,7706.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7598.47,Informer
+2024-08-07,7600.42,7592.13,7560.49,7700.19,7603.22,7600.74,7594.67,7605.4,7620.27,7632.15,7602.99,7587.78,7602.43,7620.43,7599.1,7588.53,7647.25,7581.33,7607.23,7602.89,7586.67,0.02,0.07,0.09,0.04,0.08,0.0,0.17,0.26,0.26,0.53,0.11,0.03,0.03,0.16,0.0,0.03,0.42,1.31,0.62,0.25,7560.49,7700.19,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7587.78,DilatedRNN
+2024-08-08,7582.46,7579.27,7547.9,7691.99,7590.51,7583.07,7585.2,7581.87,7610.47,7580.54,7596.98,7572.43,7580.94,7599.9,7587.52,7592.21,7644.23,7643.29,7591.49,7595.2,7583.33,0.07,0.01,0.12,0.11,0.04,0.0,0.13,0.23,0.37,0.46,0.04,0.17,0.02,0.13,0.01,0.19,0.03,1.44,0.81,0.8,7547.9,7691.99,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7583.07,TSMixerx
+2024-08-09,7572.3,7579.08,7544.76,7685.98,7581.03,7579.99,7585.11,7579.56,7609.39,7579.0,7593.55,7571.36,7577.72,7584.52,7578.71,7580.27,7640.76,7661.68,7580.79,7613.95,7583.33,0.08,0.1,0.11,0.12,0.17,0.0,0.01,0.16,0.49,0.36,0.09,0.55,0.07,0.11,0.1,0.28,0.09,1.5,0.9,1.18,7544.76,7661.68,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7584.52,DLinear
+2024-08-12,7565.19,7562.29,7545.41,7676.57,7579.53,7568.37,7583.51,7586.38,7610.8,7581.76,7591.14,7573.16,7571.12,7579.25,7574.68,7569.9,7637.37,7675.83,7579.89,7606.85,7586.67,0.13,0.28,0.19,0.19,0.24,0.0,0.11,0.19,0.6,0.26,0.04,0.55,0.08,0.06,0.04,0.34,0.22,1.47,0.95,1.46,7545.41,7676.57,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7586.38,RNN
+2024-08-13,7563.62,7566.02,7550.8,7670.48,7579.66,7581.39,7587.7,7591.05,7616.26,7595.69,7589.46,7581.39,7578.19,7586.37,7580.72,7572.45,7634.98,7685.1,7584.15,7605.62,7593.33,0.23,0.36,0.27,0.21,0.32,0.0,0.23,0.3,0.7,0.17,0.03,0.56,0.19,0.12,0.23,0.34,0.42,1.41,0.94,1.61,7550.8,7670.48,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7591.05,RNN
+2024-08-14,7576.39,7581.33,7560.34,7675.32,7581.9,7581.96,7593.26,7601.19,7625.12,7601.06,7585.96,7582.96,7591.15,7583.67,7594.52,7589.33,7632.88,7686.85,7588.09,7596.56,7590.0,0.24,0.33,0.15,0.07,0.22,0.0,0.09,0.1,0.64,0.21,0.07,0.27,0.19,0.17,0.07,0.13,0.33,1.31,0.75,1.46,7560.34,7686.85,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7589.33,TFT
+2024-08-15,7577.48,7580.18,7554.1,7682.5,7580.27,7581.07,7588.06,7594.36,7617.3,7594.63,7577.61,7574.04,7585.9,7573.04,7589.19,7595.72,7630.35,7676.25,7584.21,7588.85,7583.33,0.15,0.22,0.09,0.04,0.14,0.0,0.05,0.06,0.53,0.31,0.04,0.15,0.11,0.24,0.05,0.0,0.23,1.39,0.7,1.3,7554.1,7682.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7584.21,TiDE
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+2024-08-20,7572.15,7547.01,7526.81,7671.45,7568.6,7580.24,7569.61,7567.35,7591.95,7568.04,7561.53,7550.82,7574.94,7559.89,7564.28,7585.51,7621.06,7668.15,7564.85,7593.95,7570.0,0.1,0.06,0.1,0.05,0.03,0.0,0.28,0.16,0.26,0.6,0.33,0.29,0.04,0.18,0.11,0.14,0.05,1.31,0.65,1.27,7526.81,7671.45,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7569.61,PatchTST
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+2024-08-22,7573.07,7553.0,7529.61,7668.62,7570.14,7576.54,7578.19,7572.59,7597.05,7564.06,7557.67,7549.96,7582.79,7558.09,7571.36,7573.0,7613.63,7649.21,7570.82,7596.41,7590.0,0.02,0.01,0.03,0.04,0.07,0.0,0.31,0.2,0.32,0.57,0.26,0.31,0.13,0.0,0.05,0.2,0.12,1.26,0.54,1.01,7529.61,7668.62,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,7596.41,DeepNPTS
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+2024-08-27,7583.67,7562.52,7548.9,7658.7,7583.01,7588.14,7583.17,7588.62,7614.19,7590.1,7572.3,7571.03,7577.78,7596.16,7581.18,7582.94,7603.87,7646.62,7585.5,7587.22,7610.0,0.03,0.07,0.02,0.01,0.01,0.0,0.17,0.16,0.4,0.46,0.28,0.05,0.08,0.01,0.06,0.15,0.08,0.99,0.27,0.83,7548.9,7658.7,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,7614.19,GRU
+2024-08-28,7608.75,7573.37,7587.12,7673.3,7599.94,7608.28,7606.73,7628.21,7652.47,7664.54,7591.65,7610.38,7596.44,7602.33,7610.44,7591.02,7601.62,7649.51,7604.31,7595.06,7610.0,0.02,0.26,0.06,0.12,0.03,0.0,0.02,0.08,0.57,0.28,0.46,0.18,0.16,0.23,0.01,0.22,0.73,0.85,0.09,0.54,7573.37,7673.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,7610.38,DilatedRNN
+2024-08-29,7610.24,7581.95,7579.49,7667.2,7606.13,7611.64,7601.7,7614.17,7638.98,7657.09,7608.52,7604.72,7599.74,7592.46,7610.07,7593.12,7599.89,7651.06,7610.39,7598.34,7613.33,0.0,0.05,0.0,0.05,0.11,0.0,0.07,0.23,0.38,0.4,0.37,0.16,0.14,0.22,0.02,0.02,0.62,0.75,0.14,0.54,7579.49,7667.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,7614.17,RNN
+2024-08-30,7614.03,7597.63,7586.96,7655.72,7609.34,7627.82,7609.66,7622.84,7647.73,7683.32,7625.2,7611.68,7604.11,7604.98,7607.4,7605.44,7598.84,7655.51,7616.44,7604.42,7606.67,0.09,0.12,0.03,0.06,0.06,0.0,0.03,0.12,0.44,0.36,0.22,0.13,0.13,0.11,0.18,0.15,0.91,0.55,0.2,0.54,7586.96,7683.32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,7607.4,NLinear
+2024-09-02,7605.77,7607.48,7574.01,7657.55,7606.13,7623.34,7601.08,7617.14,7632.79,7647.45,7631.48,7601.88,7607.32,7602.92,7610.32,7598.03,7597.7,7649.4,7604.07,7593.73,7583.33,0.06,0.15,0.02,0.0,0.06,0.0,0.05,0.04,0.36,0.42,0.02,0.16,0.02,0.1,0.23,0.34,0.55,0.68,0.11,0.57,7574.01,7657.55,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.01,7574.01,LSTM
+2024-09-03,7585.53,7591.18,7541.28,7616.14,7585.56,7597.08,7575.22,7577.4,7602.02,7584.32,7620.22,7566.19,7593.28,7584.09,7584.79,7569.87,7596.02,7632.57,7579.08,7589.84,7562.5,0.01,0.11,0.09,0.0,0.14,0.0,0.25,0.02,0.22,0.58,0.07,0.06,0.1,0.21,0.15,0.46,0.02,0.4,0.14,0.62,7541.28,7632.57,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,7566.19,DilatedRNN
+2024-09-04,7568.37,7571.94,7515.98,7610.57,7565.79,7581.26,7562.25,7558.08,7581.85,7545.27,7604.56,7535.46,7576.39,7572.94,7565.45,7557.33,7593.59,7618.83,7560.98,7584.89,7535.0,0.04,0.14,0.1,0.03,0.08,0.0,0.43,0.06,0.18,0.69,0.05,0.22,0.11,0.15,0.17,0.48,0.31,0.56,0.33,0.67,7515.98,7618.83,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,7535.46,DilatedRNN
+2024-09-05,7543.76,7554.78,7480.27,7596.32,7542.46,7560.43,7542.41,7526.06,7549.62,7514.26,7580.6,7496.51,7554.42,7552.65,7529.62,7533.45,7589.9,7609.7,7536.19,7604.62,7475.0,0.19,0.23,0.1,0.02,0.02,0.0,0.63,0.12,0.08,0.84,0.15,0.81,0.14,0.14,0.22,0.49,0.39,0.7,0.61,0.87,7480.27,7609.7,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.02,0.01,0.01,0.01,0.01,0.01,0.02,0.02,0.02,7480.27,LSTM
+2024-09-06,7486.44,7535.38,7399.34,7579.63,7490.15,7530.32,7484.73,7455.8,7477.09,7467.9,7540.47,7420.37,7514.24,7518.1,7469.82,7508.77,7582.87,7581.29,7483.85,7606.65,7442.5,0.22,0.41,0.03,0.05,0.02,0.0,0.88,0.42,0.12,1.16,0.65,1.61,0.37,0.3,0.59,0.72,0.25,1.24,1.29,1.27,7399.34,7582.87,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.01,0.01,0.02,0.01,0.01,0.01,0.01,0.0,0.02,0.02,0.02,7455.8,RNN
+2024-09-09,7470.01,7515.88,7364.62,7562.42,7446.27,7498.53,7460.67,7431.29,7457.63,7439.77,7496.35,7380.39,7490.04,7478.05,7433.33,7489.86,7574.18,7557.17,7447.27,7583.34,7422.5,0.49,0.52,0.3,0.32,0.13,0.0,1.2,0.11,0.17,1.41,0.61,1.52,0.27,0.27,0.38,0.35,0.4,1.24,1.39,1.17,7364.62,7574.18,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.02,0.01,0.01,0.01,0.01,0.0,0.02,0.02,0.02,7431.29,RNN
+2024-09-10,7451.33,7504.81,7339.71,7541.21,7420.48,7477.1,7438.77,7409.71,7440.34,7417.87,7462.64,7355.09,7458.54,7445.15,7421.15,7475.16,7564.85,7527.78,7425.45,7562.5,7412.5,0.41,0.56,0.35,0.41,0.17,0.0,1.29,0.08,0.15,1.5,0.72,1.49,0.1,0.32,0.35,0.15,0.45,1.21,1.52,1.03,7339.71,7562.5,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.02,0.01,0.01,0.01,0.01,0.0,0.02,0.02,0.02,7409.71,RNN
+2024-09-11,7430.97,7491.05,7330.97,7519.37,7404.28,7462.26,7420.89,7398.85,7436.14,7408.15,7431.14,7340.45,7432.55,7418.98,7415.28,7464.61,7555.64,7498.71,7417.44,7535.0,7387.5,0.21,0.43,0.18,0.36,0.14,0.0,1.22,0.16,0.07,1.35,0.81,1.4,0.02,0.45,0.42,0.0,0.31,1.19,1.68,0.91,7330.97,7535.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.02,0.01,0.01,0.01,0.01,0.0,0.02,0.02,0.02,7398.85,RNN
+2024-09-12,7398.0,7478.74,7303.67,7492.92,7382.31,7444.67,7391.09,7382.94,7411.65,7396.88,7397.14,7313.15,7402.38,7400.79,7386.94,7448.76,7545.54,7460.06,7395.3,7475.01,7380.0,0.15,0.2,0.04,0.21,0.09,0.0,1.15,0.04,0.18,1.28,1.09,1.04,0.06,0.69,0.63,0.01,0.02,1.28,1.99,0.84,7303.67,7492.92,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.0,0.02,0.02,0.01,7382.31,TSMixer
+2024-09-13,7388.71,7460.03,7300.95,7464.13,7369.45,7424.93,7380.86,7379.25,7411.88,7392.85,7374.15,7309.57,7382.1,7401.54,7385.04,7438.55,7535.68,7439.73,7384.62,7442.51,7395.0,0.05,0.13,0.06,0.26,0.11,0.0,1.07,0.17,0.31,1.19,0.97,0.73,0.09,0.67,0.49,0.2,0.06,1.02,1.99,0.69,7300.95,7464.13,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.02,0.01,7392.85,TCN
+2024-09-14,7403.95,7464.0,7318.92,7450.39,7380.9,7417.35,7390.87,7396.28,7429.07,7406.03,7371.11,7327.5,7375.1,7403.94,7403.98,7435.49,7527.23,7421.01,7393.19,7422.53,7375.0,0.0,0.1,0.15,0.31,0.18,0.0,1.03,0.0,0.34,1.15,0.81,0.25,0.39,0.43,0.18,0.44,0.03,0.63,1.67,0.23,7318.92,7464.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.02,0.01,7375.1,MLP
+2024-09-18,7378.25,7450.51,7296.64,7420.48,7374.33,7397.35,7365.31,7369.65,7403.89,7405.31,7367.04,7307.74,7352.11,7382.56,7384.77,7418.46,7518.24,7376.68,7382.2,7412.61,7342.5,0.09,0.12,0.05,0.05,0.18,0.0,0.96,0.06,0.35,1.11,0.98,0.47,0.35,0.54,0.26,0.15,0.37,0.57,1.9,0.02,7296.64,7450.51,0.01,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.02,0.0,7352.11,MLP
+2024-09-19,7345.8,7424.07,7273.03,7383.78,7351.39,7367.71,7348.02,7343.67,7377.78,7405.74,7364.49,7286.64,7340.79,7371.55,7346.23,7404.98,7508.11,7313.24,7357.08,7392.25,7325.0,0.01,0.03,0.15,0.08,0.03,0.0,0.81,0.35,0.44,0.99,1.07,0.63,0.07,0.81,0.3,0.25,0.82,0.52,2.21,0.44,7273.03,7424.07,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.02,0.0,7313.24,MLPMultivariate
+2024-09-20,7331.34,7411.4,7261.47,7359.5,7333.1,7352.78,7340.24,7332.29,7366.28,7407.88,7366.1,7275.0,7339.29,7357.37,7336.36,7381.77,7497.51,7277.24,7337.64,7389.01,7340.0,0.07,0.01,0.09,0.02,0.12,0.0,0.77,0.36,0.48,0.95,1.09,0.79,0.11,0.69,0.29,0.47,1.04,0.38,2.27,0.74,7261.47,7411.4,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.02,0.01,7340.24,PatchTST
+2024-09-23,7350.17,7409.47,7273.6,7360.28,7338.57,7354.65,7355.94,7342.36,7383.53,7413.67,7372.86,7285.44,7350.24,7359.13,7346.39,7390.63,7488.3,7253.74,7336.25,7363.92,7310.0,0.05,0.11,0.19,0.16,0.08,0.0,0.88,0.12,0.45,1.04,0.81,0.19,0.0,0.55,0.06,0.31,0.86,0.14,1.88,1.31,7253.74,7413.67,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.01,7285.44,DilatedRNN
+2024-09-24,7334.33,7387.52,7250.34,7333.1,7321.56,7341.2,7323.07,7313.94,7351.58,7405.47,7369.32,7258.87,7332.21,7339.95,7319.59,7372.23,7478.05,7233.88,7309.53,7330.44,7280.0,0.2,0.28,0.34,0.17,0.15,0.0,1.03,0.08,0.24,1.15,0.73,0.05,0.03,0.52,0.09,0.48,0.97,0.02,1.96,1.37,7233.88,7405.47,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.01,0.03,0.01,7258.87,DilatedRNN
+2024-09-25,7331.68,7358.95,7233.76,7294.82,7295.02,7325.46,7313.92,7295.67,7333.57,7389.7,7358.18,7239.93,7329.29,7318.18,7284.22,7352.1,7466.86,7208.89,7292.13,7300.88,7315.0,0.65,0.49,0.54,0.5,0.24,0.0,1.25,0.18,0.03,1.34,0.37,0.42,0.03,0.28,0.08,0.36,0.79,0.5,1.84,1.67,7233.76,7389.7,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.02,0.01,7313.92,PatchTST
+2024-09-26,7383.23,7354.28,7258.63,7322.43,7311.57,7337.57,7346.59,7334.2,7368.18,7395.19,7360.13,7262.58,7348.37,7337.87,7321.63,7365.79,7457.88,7369.88,7315.96,7309.91,7310.0,0.83,0.66,0.91,0.97,0.5,0.0,1.63,0.61,0.2,1.69,0.39,0.99,0.47,0.24,0.62,0.31,0.16,0.82,1.01,0.18,7309.91,7457.88,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.02,0.01,7309.91,DeepNPTS
+2024-09-27,7395.39,7342.84,7252.57,7349.44,7311.78,7332.55,7328.61,7316.26,7355.87,7383.46,7355.37,7250.85,7333.29,7314.8,7318.06,7363.19,7449.12,7426.95,7307.61,7323.36,7310.0,1.05,1.07,1.19,1.13,0.9,0.0,1.95,1.09,0.53,1.93,0.71,0.97,0.84,0.44,0.85,0.54,0.16,0.62,0.73,0.43,7307.61,7449.12,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.02,0.02,7311.78,TSMixer
+2024-09-29,7404.69,7328.01,7255.95,7412.08,7310.44,7339.92,7335.89,7322.03,7360.7,7391.28,7348.53,7255.08,7324.23,7308.09,7314.78,7356.94,7440.11,7459.6,7314.78,7323.36,7365.0,1.21,1.12,1.21,1.27,0.93,0.0,2.02,1.3,0.59,2.01,1.04,1.1,1.09,0.64,0.87,0.76,0.18,0.1,0.48,0.74,7308.09,7459.6,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7360.7,GRU
+2024-09-30,7426.12,7341.11,7300.09,7456.35,7351.28,7360.36,7371.79,7386.81,7413.94,7423.46,7354.23,7305.8,7350.79,7337.02,7372.67,7375.01,7433.62,7526.69,7362.78,7362.27,7420.0,0.72,0.53,0.85,1.01,0.73,0.0,1.62,1.2,0.16,1.7,1.14,0.86,1.01,0.69,0.89,0.97,0.04,0.41,0.1,1.35,7337.02,7526.69,0.01,0.0,0.01,0.01,0.01,0.0,0.02,0.01,0.0,0.02,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.0,0.0,0.01,7423.46,TCN
+2024-10-08,7406.37,7396.05,7363.97,7515.23,7401.51,7391.97,7409.69,7428.8,7463.3,7450.62,7382.85,7368.46,7376.8,7366.11,7426.31,7404.72,7429.4,7614.2,7412.17,7419.09,7602.5,0.27,0.3,0.08,0.07,0.04,0.0,0.51,0.54,0.77,0.57,0.14,0.17,0.4,0.02,0.19,0.32,0.6,1.47,0.31,2.81,7363.97,7515.23,0.02,0.02,0.03,0.03,0.03,0.03,0.03,0.03,0.02,0.03,0.03,0.02,0.03,0.03,0.03,0.03,0.02,0.01,0.02,0.0,7614.2,MLPMultivariate
+2024-10-09,7577.33,7574.57,7625.6,7661.65,7544.47,7607.6,7631.74,7679.46,7699.37,7713.1,7580.36,7635.88,7586.54,7484.3,7607.45,7604.1,7429.48,7766.63,7573.71,7602.24,7602.5,0.4,1.35,0.05,0.43,0.72,0.0,0.77,1.23,1.61,0.64,0.04,0.33,0.12,0.35,0.4,0.04,1.79,1.11,1.95,2.5,7484.3,7679.46,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.02,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.02,0.02,7602.24,DeepNPTS
+2024-10-10,7576.0,7601.33,7581.64,7719.31,7608.25,7677.35,7632.34,7639.99,7633.43,7766.71,7661.03,7634.72,7640.36,7542.83,7609.24,7617.74,7429.69,7786.16,7604.18,7602.37,7585.0,0.44,0.84,0.37,0.43,0.74,0.0,0.78,0.44,0.76,0.07,0.33,0.35,0.85,0.55,1.34,1.12,2.52,1.89,1.93,2.77,7542.83,7677.35,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.02,0.02,0.02,0.03,7581.64,LSTM
+2024-10-11,7631.65,7624.48,7575.07,7766.2,7609.2,7714.27,7629.44,7599.98,7624.22,7788.7,7691.29,7618.88,7651.66,7572.18,7587.86,7623.61,7429.71,7764.22,7600.31,7584.85,7575.0,0.57,0.41,0.41,0.29,0.03,0.0,0.17,0.78,0.1,0.74,0.09,0.61,0.26,0.11,1.08,0.78,2.06,1.76,2.65,1.74,7572.18,7714.27,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.02,0.02,0.03,0.03,0.02,0.02,7575.07,LSTM
+2024-10-12,7653.07,7598.33,7548.5,7847.1,7585.18,7706.53,7607.08,7603.88,7602.92,7740.27,7700.39,7589.1,7663.18,7571.87,7574.65,7629.57,7429.59,7716.69,7568.07,7574.84,7576.67,1.02,0.64,1.11,0.89,0.6,0.0,0.84,1.06,0.66,1.37,0.72,1.02,0.13,0.31,0.7,0.62,1.14,2.54,2.92,0.83,7548.5,7740.27,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.02,0.02,0.02,0.04,0.02,0.02,7574.84,DeepNPTS
+2024-10-14,7691.78,7608.75,7548.03,7871.49,7552.13,7690.78,7583.72,7553.81,7607.37,7730.31,7696.8,7583.52,7677.65,7557.6,7562.15,7651.37,7429.44,7670.88,7546.73,7576.1,7565.0,1.69,1.79,1.89,1.82,1.4,0.0,1.41,1.74,1.1,1.87,1.08,1.5,0.18,0.53,0.01,0.07,0.5,2.34,3.41,0.27,7583.52,7730.31,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.02,0.02,0.02,0.04,0.02,0.01,7562.15,NLinear
+2024-10-15,7639.02,7604.74,7524.41,7792.94,7538.07,7647.37,7550.89,7554.31,7587.3,7702.48,7678.15,7562.07,7640.33,7526.24,7536.04,7653.55,7429.02,7615.24,7536.11,7564.45,7527.5,1.35,1.11,1.35,1.32,1.15,0.0,1.01,1.48,0.68,1.5,0.45,0.98,0.02,0.19,0.11,0.51,0.83,2.01,2.75,0.31,7526.24,7702.48,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.02,0.02,0.02,0.02,0.04,0.01,0.01,7526.24,DLinear
+2024-10-16,7560.16,7584.03,7472.19,7524.15,7510.13,7557.71,7500.61,7525.6,7538.71,7580.69,7620.62,7490.21,7571.23,7510.21,7483.04,7574.04,7427.96,7569.48,7510.9,7522.37,7477.5,1.02,0.46,0.65,0.66,0.79,0.0,0.93,0.66,0.28,1.16,0.32,0.5,0.15,0.18,0.03,0.8,0.27,0.48,1.75,0.12,7472.19,7620.62,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.02,0.01,0.01,0.01,0.01,7472.19,LSTM
+2024-10-17,7497.05,7571.56,7406.36,7436.05,7462.23,7506.3,7433.55,7466.97,7484.21,7495.48,7551.6,7417.59,7502.19,7486.54,7451.01,7481.14,7425.86,7531.56,7464.61,7505.66,7467.5,0.61,0.4,0.43,0.46,0.85,0.0,1.06,0.14,0.17,1.21,0.99,0.11,0.07,0.21,0.12,0.73,0.02,0.81,0.95,0.46,7406.36,7571.56,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,7466.97,RNN
+2024-10-18,7485.83,7576.53,7400.38,7405.13,7439.71,7492.0,7417.06,7460.72,7487.02,7473.88,7503.86,7368.89,7465.12,7435.8,7465.36,7460.77,7423.55,7513.17,7448.06,7457.38,7370.0,0.27,0.34,0.5,0.62,0.92,0.0,1.56,0.67,0.02,1.14,1.21,0.38,0.28,0.33,0.08,0.24,0.16,1.08,0.83,0.37,7400.38,7576.53,0.01,0.01,0.01,0.01,0.01,0.02,0.0,0.01,0.02,0.0,0.03,0.01,0.01,0.01,0.02,0.02,0.01,0.0,0.01,0.02,7368.89,DilatedRNN
+2024-10-21,7344.19,7448.06,7278.98,7367.94,7369.4,7422.88,7311.05,7344.49,7375.82,7434.49,7406.12,7267.31,7378.31,7359.09,7361.07,7360.86,7418.39,7455.82,7359.83,7457.08,7362.5,0.23,0.0,0.21,0.34,0.45,0.0,1.05,0.2,0.43,0.89,1.41,1.54,0.46,0.23,1.07,0.84,1.23,0.32,1.01,1.52,7267.31,7448.06,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.01,7361.07,NLinear
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+2024-10-24,7327.36,7330.89,7282.53,7334.39,7335.02,7320.17,7344.86,7348.92,7396.87,7389.91,7289.94,7275.05,7332.82,7332.66,7343.01,7344.94,7401.82,7432.15,7340.73,7461.51,7352.5,0.21,0.29,0.18,0.1,0.24,0.0,0.71,0.07,0.95,0.61,0.05,1.83,0.07,0.24,0.1,0.51,0.85,0.1,1.02,1.43,7275.05,7432.15,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,7348.92,RNN
+2024-10-25,7343.94,7307.17,7278.58,7353.38,7347.83,7335.72,7367.26,7362.14,7388.07,7392.13,7273.09,7271.2,7327.52,7320.14,7359.31,7334.4,7396.39,7461.64,7356.27,7405.27,7372.5,0.21,0.25,0.17,0.05,0.32,0.0,0.99,0.32,0.6,0.89,0.5,0.84,0.22,0.13,0.11,0.96,0.66,0.13,0.71,1.6,7271.2,7405.27,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.0,0.0,0.0,0.01,7367.26,PatchTST
+2024-10-28,7348.35,7346.41,7299.95,7382.28,7373.17,7332.05,7394.25,7378.33,7413.72,7409.43,7278.91,7304.22,7355.17,7345.4,7380.76,7341.12,7391.81,7485.78,7385.35,7352.0,7373.33,0.44,0.41,0.5,0.34,0.62,0.0,0.6,0.04,0.89,0.66,0.03,0.05,0.09,0.1,0.22,0.95,0.83,0.46,0.59,1.87,7278.91,7413.72,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.02,7373.17,TSMixer
+2024-10-29,7362.26,7347.62,7299.38,7403.71,7382.63,7372.1,7388.31,7376.75,7408.46,7425.13,7307.02,7317.3,7360.51,7394.13,7388.48,7339.41,7387.2,7506.54,7374.2,7368.01,7380.0,0.36,0.2,0.16,0.28,0.35,0.0,0.61,0.43,0.63,0.85,0.2,0.08,0.02,0.31,0.13,0.75,0.85,0.56,0.34,1.96,7299.38,7425.13,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.02,7382.63,TSMixer
+2024-10-30,7368.73,7372.31,7309.24,7410.82,7385.75,7428.36,7376.78,7383.46,7418.25,7445.98,7354.4,7342.89,7363.36,7410.4,7387.01,7346.05,7383.02,7505.69,7359.37,7437.75,7330.0,0.25,0.2,0.13,0.23,0.11,0.0,0.35,0.57,0.67,0.81,0.05,0.94,0.07,0.31,0.81,0.19,1.05,0.57,0.19,1.86,7309.24,7445.98,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.02,0.01,0.01,0.02,7342.89,DilatedRNN
+2024-10-31,7311.11,7337.7,7264.33,7417.51,7354.56,7416.21,7295.6,7331.68,7363.08,7446.37,7349.71,7310.97,7345.95,7400.75,7339.24,7304.52,7377.33,7490.8,7331.52,7393.39,7340.0,0.38,0.28,0.28,0.59,0.21,0.0,0.0,1.23,0.71,0.64,0.36,1.13,0.48,0.09,1.44,0.53,1.85,1.46,0.91,2.46,7264.33,7417.51,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.02,7339.24,NLinear
+2024-11-01,7325.45,7340.67,7277.19,7403.85,7350.09,7391.7,7325.08,7346.34,7386.1,7448.67,7361.61,7319.58,7370.1,7385.94,7339.78,7319.82,7372.31,7485.72,7337.9,7388.84,7363.33,0.2,0.29,0.17,0.34,0.01,0.0,0.08,0.83,0.83,0.66,0.21,0.87,0.61,0.08,0.9,0.49,1.68,1.07,0.64,2.19,7277.19,7403.85,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.02,7361.61,BiTCN
+2024-11-04,7380.95,7383.65,7292.43,7383.24,7362.64,7391.41,7347.38,7368.79,7402.84,7453.22,7370.16,7335.67,7402.01,7359.02,7359.95,7345.06,7368.38,7497.59,7359.38,7382.9,7360.0,0.28,0.16,0.29,0.25,0.45,0.0,0.61,0.3,0.3,1.2,0.04,0.03,0.29,0.49,0.14,0.15,0.98,0.03,0.17,1.58,7292.43,7453.22,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.02,7359.95,NLinear
+2024-11-05,7384.76,7385.63,7289.12,7346.33,7359.37,7429.52,7351.29,7355.62,7396.87,7454.93,7371.16,7325.31,7409.42,7374.6,7372.07,7351.83,7364.63,7481.09,7351.21,7367.23,7357.5,0.17,0.39,0.45,0.34,0.45,0.0,0.81,0.14,0.16,1.3,0.01,0.24,0.33,0.45,0.61,0.18,0.95,0.52,0.27,1.3,7289.12,7481.09,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.0,0.02,7359.37,TSMixer
+2024-11-06,7372.14,7355.21,7288.76,7314.96,7358.52,7411.56,7351.78,7368.22,7396.45,7452.88,7354.85,7322.02,7400.71,7361.08,7378.69,7358.67,7361.11,7474.57,7344.45,7366.55,7383.33,0.09,0.05,0.38,0.18,0.28,0.0,0.68,0.15,0.33,1.13,0.23,0.08,0.39,0.18,0.53,0.23,1.1,0.78,0.15,1.39,7288.76,7474.57,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,7378.69,NLinear
+2024-11-07,7394.49,7360.17,7314.98,7313.69,7372.23,7396.45,7372.83,7393.67,7424.33,7451.24,7358.23,7334.24,7417.74,7365.87,7401.06,7365.66,7359.13,7468.38,7365.83,7385.99,7366.67,0.09,0.01,0.39,0.3,0.29,0.0,0.81,0.39,0.4,1.08,0.46,0.11,0.31,0.39,0.03,0.49,0.77,1.09,0.48,1.0,7313.69,7468.38,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.0,0.01,7365.87,DLinear
+2024-11-08,7367.07,7340.66,7293.82,7316.41,7367.46,7382.51,7359.92,7362.61,7398.7,7438.23,7354.98,7304.15,7401.83,7383.2,7376.13,7365.23,7357.25,7458.3,7358.59,7369.13,7383.33,0.12,0.06,0.12,0.01,0.1,0.0,0.85,0.22,0.43,0.99,0.36,0.03,0.47,0.02,0.21,0.16,0.97,0.69,0.13,1.24,7293.82,7458.3,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,7383.2,DLinear
+2024-11-11,7385.33,7367.98,7315.88,7312.9,7378.33,7404.64,7387.22,7388.8,7424.18,7437.92,7365.14,7305.25,7419.91,7362.62,7385.12,7370.33,7356.28,7474.63,7378.79,7380.81,7340.0,0.0,0.05,0.09,0.09,0.03,0.0,1.08,0.31,0.53,0.94,0.23,0.06,0.47,0.2,0.26,0.27,0.71,0.98,0.39,1.21,7305.25,7474.63,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.01,0.0,0.0,0.02,7356.28,StemGNN
+2024-11-12,7348.61,7291.9,7270.91,7276.3,7360.35,7418.77,7340.51,7341.66,7370.75,7421.61,7353.11,7263.97,7401.87,7368.74,7336.24,7330.87,7354.22,7450.79,7358.86,7365.14,7306.67,0.17,0.09,0.14,0.16,0.11,0.0,1.15,0.27,0.3,1.06,0.77,0.23,0.72,0.24,0.95,0.06,0.99,0.98,0.08,1.39,7263.97,7450.79,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.02,0.01,0.02,0.0,0.01,0.02,7291.9,Informer
+2024-11-13,7332.29,7268.64,7251.3,7254.86,7321.8,7370.7,7316.54,7309.23,7352.06,7407.6,7345.42,7241.63,7403.21,7337.77,7293.72,7283.12,7351.63,7422.7,7318.66,7366.33,7323.33,0.53,0.31,0.19,0.14,0.21,0.0,1.24,0.07,0.27,1.1,0.87,0.46,0.97,0.67,0.52,0.18,1.03,1.06,0.26,1.23,7241.63,7422.7,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.01,7321.8,TSMixer
+2024-11-14,7344.44,7272.3,7257.76,7244.21,7333.12,7360.61,7371.92,7303.81,7360.68,7413.77,7340.82,7254.99,7417.81,7357.29,7319.99,7274.54,7348.14,7390.92,7325.41,7400.55,7353.33,0.33,0.55,0.26,0.15,0.37,0.0,1.22,0.17,0.22,1.18,0.98,0.76,1.0,0.95,0.22,0.05,0.94,1.36,0.05,0.63,7244.21,7417.81,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.01,7357.29,DLinear
+2024-11-15,7319.74,7273.8,7268.91,7230.74,7333.59,7348.71,7348.61,7284.0,7349.73,7441.16,7343.64,7265.12,7418.84,7321.15,7309.22,7271.93,7337.24,7383.45,7336.21,7391.44,7393.33,0.14,0.49,0.23,0.19,0.39,0.0,0.75,0.02,0.41,0.69,0.63,0.98,1.35,0.65,0.4,0.33,1.66,1.22,0.24,0.87,7230.74,7418.84,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.01,0.01,0.02,0.02,0.0,0.0,0.02,0.01,0.01,0.01,0.02,0.01,0.0,7391.44,DeepNPTS
+2024-11-18,7317.36,7293.89,7282.1,7208.8,7330.95,7366.03,7319.87,7297.49,7368.11,7442.79,7338.93,7287.87,7411.82,7370.53,7321.93,7272.12,7360.05,7365.12,7326.46,7397.05,7363.33,0.06,0.27,0.12,0.19,0.03,0.0,0.4,0.73,0.69,0.48,0.32,1.09,1.29,0.62,0.67,0.29,1.71,1.48,0.58,0.65,7208.8,7411.82,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.02,0.0,0.0,7365.12,MLPMultivariate
+2024-11-19,7307.74,7295.89,7310.31,7191.16,7332.0,7325.75,7319.12,7335.5,7371.58,7436.03,7333.44,7305.78,7369.13,7344.6,7332.86,7272.76,7349.71,7334.35,7334.74,7399.99,7370.0,0.34,0.38,0.37,0.33,0.16,0.0,0.03,0.5,0.87,0.04,0.16,1.26,0.84,0.48,0.25,0.35,1.76,1.6,0.57,0.36,7272.76,7399.99,0.01,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.02,0.0,0.0,7369.13,MLP
+2024-11-20,7337.59,7316.53,7313.05,7342.47,7344.39,7306.89,7350.64,7378.3,7405.32,7418.54,7321.07,7320.57,7338.04,7387.81,7361.4,7362.29,7351.25,7331.53,7354.71,7376.74,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2024-11-21,7317.17,7307.52,7316.97,7328.65,7332.21,7313.82,7338.19,7370.33,7411.06,7428.94,7317.81,7327.25,7336.69,7338.95,7351.55,7367.47,7347.77,7312.24,7317.22,7363.68,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2024-11-22,7297.24,7303.13,7326.41,7326.86,7312.23,7317.62,7323.69,7346.49,7403.67,7445.02,7312.61,7330.36,7338.74,7334.72,7364.72,7364.16,7336.86,7308.51,7293.33,7345.68,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2024-11-25,7303.15,7296.9,7344.72,7335.47,7295.32,7333.21,7318.15,7358.58,7420.51,7441.58,7302.02,7356.28,7322.75,7303.17,7395.47,7359.94,7359.68,7302.15,7295.56,7386.05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2024-11-26,7291.95,7311.98,7373.82,7342.08,7280.39,7364.26,7291.85,7386.51,7420.56,7449.44,7294.83,7374.35,7319.28,7310.3,7329.59,7357.71,7349.34,7286.71,7291.14,7341.73,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/juxitingdataset/testandpredict_groupby.csv b/juxitingdataset/testandpredict_groupby.csv
index 0323c68..9e83240 100644
--- a/juxitingdataset/testandpredict_groupby.csv
+++ b/juxitingdataset/testandpredict_groupby.csv
@@ -1,206 +1,201 @@
-ds,NHITS,Informer,LSTM,iTransformer,TSMixer,TSMixerx,PatchTST,RNN,GRU,TCN,BiTCN,DilatedRNN,MLP,DLinear,NLinear,TFT,StemGNN,MLPMultivariate,TiDE,DeepNPTS,y
-2024-01-30,7314.25,7335.76,7255.32,7375.61,7296.98,7304.24,7288.53,7316.8,7357.77,7406.36,7330.32,7284.31,7336.16,7282.57,7305.31,7289.84,7306.12,7295.54,7297.12,7304.88,7315.0
-2024-01-31,7313.85,7333.85,7260.27,7376.32,7306.93,7303.91,7291.97,7327.11,7364.25,7409.82,7340.18,7298.74,7321.85,7294.2,7298.47,7302.1,7304.55,7299.61,7302.49,7313.87,7302.5
-2024-02-01,7297.0,7335.21,7250.98,7385.46,7298.8,7304.56,7284.13,7314.06,7351.66,7406.99,7334.74,7290.45,7319.52,7291.02,7287.84,7290.86,7303.42,7300.2,7301.71,7297.39,7275.0
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diff --git a/juxitingdataset/历史价格-预测值.png b/juxitingdataset/历史价格-预测值.png
index 4177b96..2b630e6 100644
Binary files a/juxitingdataset/历史价格-预测值.png and b/juxitingdataset/历史价格-预测值.png differ
diff --git a/juxitingdataset/原油指标数据.xlsx b/juxitingdataset/原油指标数据.xlsx
deleted file mode 100644
index eb6964e..0000000
Binary files a/juxitingdataset/原油指标数据.xlsx and /dev/null differ
diff --git a/juxitingdataset/特征频度统计.csv b/juxitingdataset/特征频度统计.csv
index 8c63e53..d16524a 100644
--- a/juxitingdataset/特征频度统计.csv
+++ b/juxitingdataset/特征频度统计.csv
@@ -1,4 +1,4 @@
-日度(218),周度(94),21天(1)
+日度(218),周度(94),84天(1)
 PP主力收盘价拟合残差/丙烷 CP M1,PE注塑开工率/周,中国：华东地区：市场平均价：BOPP厚光膜
 华南聚丙烯基差（折盘面收盘价）,PP：看跌比例：中国（周）,
 华北聚丙烯基差（折盘面收盘价）,PP：看平比例：中国（周）,
diff --git a/juxitingdataset/特征频度统计.txt b/juxitingdataset/特征频度统计.txt
index d1c93ae..26d61e0 100644
--- a/juxitingdataset/特征频度统计.txt
+++ b/juxitingdataset/特征频度统计.txt
@@ -1,4 +1,4 @@
-特征信息：总共有311个,日度(218),周度(94),21天(1), 详看 附1、特征列表
+特征信息：总共有311个,日度(218),周度(94),84天(1), 详看 附1、特征列表
         数据特征工程：
     1. 数据日期排序，新日期在最后
     2. 删除空列，特征数据列没有值，就删除
diff --git a/juxitingdataset/预测值表格.png b/juxitingdataset/预测值表格.png
index d1ec9fe..9b106fc 100644
Binary files a/juxitingdataset/预测值表格.png and b/juxitingdataset/预测值表格.png differ
diff --git a/测试环境登录接口调试.ipynb b/测试环境登录接口调试.ipynb
index 8e28479..5e73a28 100644
--- a/测试环境登录接口调试.ipynb
+++ b/测试环境登录接口调试.ipynb
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "83c81b9e",
    "metadata": {},
    "outputs": [
@@ -25,7 +25,7 @@
        "'http://192.168.100.53:8080/jingbo-dev/api/server/login'"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "2b330ee3-c006-4ab1-8558-59c51ac8d86f",
    "metadata": {},
    "outputs": [
@@ -51,7 +51,7 @@
        " 'funcOperation': '获取token'}"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "dcb6100a-ed2b-4077-a1a9-361c6cb565f9",
    "metadata": {},
    "outputs": [],
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "id": "22c0c7c4",
    "metadata": {},
    "outputs": [
@@ -87,7 +87,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'confirmFlg': False, 'data': {'accessToken': 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfdGVzdCIsInRoIjoiOGE0NTc3ZGJkOTE5Njc1NzU4ZDU3OTk5YTFlODkxZmUiLCJsdCI6ImFwaSIsImlzcyI6IiIsInRtIjoiUEMiLCJleHAiOjE3MzIwMTYwNTQsImp0aSI6IjE5NjhkOTdiMDA1YzQ0OTI5NTg5ZDQ1ZWE3NjhlMWIwIn0.jZqjKms_oQfej5RFqT-If0sBqdDgbu-m8HBfS2myjQo', 'md5Token': 'e443264891e67c98ff50ca317239d31b'}, 'status': True}\n"
+      "{'confirmFlg': False, 'data': {'accessToken': 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfdGVzdCIsInRoIjoiOGE0NTc3ZGJkOTE5Njc1NzU4ZDU3OTk5YTFlODkxZmUiLCJsdCI6ImFwaSIsImlzcyI6IiIsInRtIjoiUEMiLCJleHAiOjE3MzIxMDcwNDUsImp0aSI6ImM0Njk5ZGY3YWYwMTQ1Y2I4NWUzYjJhZTg2ZjMzYzRiIn0.EuDSlyPRNA3ULUyNHCKpj2nZCCyRqpOY72A_qkoSkJs', 'md5Token': 'af20ab3fd063d5a7e1929d1e6d393f9e'}, 'status': True}\n"
      ]
     }
    ],
@@ -97,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "id": "12077ead",
    "metadata": {},
    "outputs": [],
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 7,
    "id": "a7ae21d1",
    "metadata": {},
    "outputs": [],
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 8,
    "id": "3e1a9e67",
    "metadata": {},
    "outputs": [
@@ -151,177 +151,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "--- Logging error ---\n",
-      "Traceback (most recent call last):\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\logging\\handlers.py\", line 74, in emit\n",
-      "    self.doRollover()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\logging\\handlers.py\", line 179, in doRollover\n",
-      "    self.rotate(self.baseFilename, dfn)\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\logging\\handlers.py\", line 115, in rotate\n",
-      "    os.rename(source, dest)\n",
-      "PermissionError: [WinError 32] 另一个程序正在使用此文件，进程无法访问。: 'd:\\\\code\\\\PriceForecast\\\\logs\\\\pricepredict.log' -> 'd:\\\\code\\\\PriceForecast\\\\logs\\\\pricepredict.log.1'\n",
-      "Call stack:\n",
-      "  File \"<frozen runpy>\", line 198, in _run_module_as_main\n",
-      "  File \"<frozen runpy>\", line 88, in _run_code\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel_launcher.py\", line 17, in <module>\n",
-      "    app.launch_new_instance()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\traitlets\\config\\application.py\", line 992, in launch_instance\n",
-      "    app.start()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 701, in start\n",
-      "    self.io_loop.start()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 195, in start\n",
-      "    self.asyncio_loop.run_forever()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\asyncio\\windows_events.py\", line 321, in run_forever\n",
-      "    super().run_forever()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\asyncio\\base_events.py\", line 607, in run_forever\n",
-      "    self._run_once()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\asyncio\\base_events.py\", line 1922, in _run_once\n",
-      "    handle._run()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\asyncio\\events.py\", line 80, in _run\n",
-      "    self._context.run(self._callback, *self._args)\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in dispatch_queue\n",
-      "    await self.process_one()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 523, in process_one\n",
-      "    await dispatch(*args)\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 429, in dispatch_shell\n",
-      "    await result\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 767, in execute_request\n",
-      "    reply_content = await reply_content\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 429, in do_execute\n",
-      "    res = shell.run_cell(\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n",
-      "    return super().run_cell(*args, **kwargs)\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3051, in run_cell\n",
-      "    result = self._run_cell(\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3106, in _run_cell\n",
-      "    result = runner(coro)\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n",
-      "    coro.send(None)\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3311, in run_cell_async\n",
-      "    has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3493, in run_ast_nodes\n",
-      "    if await self.run_code(code, result, async_=asy):\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3553, in run_code\n",
-      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
-      "  File \"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10336\\2387189848.py\", line 4, in <module>\n",
-      "    logger.info(\"预警上传中...\")\n",
-      "Message: '预警上传中...'\n",
-      "Arguments: ()\n",
       "预警上传中...\n",
-      "--- Logging error ---\n",
-      "Traceback (most recent call last):\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\logging\\handlers.py\", line 74, in emit\n",
-      "    self.doRollover()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\logging\\handlers.py\", line 179, in doRollover\n",
-      "    self.rotate(self.baseFilename, dfn)\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\logging\\handlers.py\", line 115, in rotate\n",
-      "    os.rename(source, dest)\n",
-      "PermissionError: [WinError 32] 另一个程序正在使用此文件，进程无法访问。: 'd:\\\\code\\\\PriceForecast\\\\logs\\\\pricepredict.log' -> 'd:\\\\code\\\\PriceForecast\\\\logs\\\\pricepredict.log.1'\n",
-      "Call stack:\n",
-      "  File \"<frozen runpy>\", line 198, in _run_module_as_main\n",
-      "  File \"<frozen runpy>\", line 88, in _run_code\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel_launcher.py\", line 17, in <module>\n",
-      "    app.launch_new_instance()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\traitlets\\config\\application.py\", line 992, in launch_instance\n",
-      "    app.start()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 701, in start\n",
-      "    self.io_loop.start()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 195, in start\n",
-      "    self.asyncio_loop.run_forever()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\asyncio\\windows_events.py\", line 321, in run_forever\n",
-      "    super().run_forever()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\asyncio\\base_events.py\", line 607, in run_forever\n",
-      "    self._run_once()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\asyncio\\base_events.py\", line 1922, in _run_once\n",
-      "    handle._run()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\asyncio\\events.py\", line 80, in _run\n",
-      "    self._context.run(self._callback, *self._args)\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in dispatch_queue\n",
-      "    await self.process_one()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 523, in process_one\n",
-      "    await dispatch(*args)\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 429, in dispatch_shell\n",
-      "    await result\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 767, in execute_request\n",
-      "    reply_content = await reply_content\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 429, in do_execute\n",
-      "    res = shell.run_cell(\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n",
-      "    return super().run_cell(*args, **kwargs)\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3051, in run_cell\n",
-      "    result = self._run_cell(\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3106, in _run_cell\n",
-      "    result = runner(coro)\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n",
-      "    coro.send(None)\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3311, in run_cell_async\n",
-      "    has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3493, in run_ast_nodes\n",
-      "    if await self.run_code(code, result, async_=asy):\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3553, in run_code\n",
-      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
-      "  File \"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10336\\2387189848.py\", line 5, in <module>\n",
-      "    logger.info(f\"token:{token}\")\n",
-      "Message: 'token:eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfdGVzdCIsInRoIjoiOGE0NTc3ZGJkOTE5Njc1NzU4ZDU3OTk5YTFlODkxZmUiLCJsdCI6ImFwaSIsImlzcyI6IiIsInRtIjoiUEMiLCJleHAiOjE3MzIwMTYwNTQsImp0aSI6IjE5NjhkOTdiMDA1YzQ0OTI5NTg5ZDQ1ZWE3NjhlMWIwIn0.jZqjKms_oQfej5RFqT-If0sBqdDgbu-m8HBfS2myjQo'\n",
-      "Arguments: ()\n",
-      "token:eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfdGVzdCIsInRoIjoiOGE0NTc3ZGJkOTE5Njc1NzU4ZDU3OTk5YTFlODkxZmUiLCJsdCI6ImFwaSIsImlzcyI6IiIsInRtIjoiUEMiLCJleHAiOjE3MzIwMTYwNTQsImp0aSI6IjE5NjhkOTdiMDA1YzQ0OTI5NTg5ZDQ1ZWE3NjhlMWIwIn0.jZqjKms_oQfej5RFqT-If0sBqdDgbu-m8HBfS2myjQo\n",
-      "--- Logging error ---\n",
-      "Traceback (most recent call last):\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\logging\\handlers.py\", line 74, in emit\n",
-      "    self.doRollover()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\logging\\handlers.py\", line 179, in doRollover\n",
-      "    self.rotate(self.baseFilename, dfn)\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\logging\\handlers.py\", line 115, in rotate\n",
-      "    os.rename(source, dest)\n",
-      "PermissionError: [WinError 32] 另一个程序正在使用此文件，进程无法访问。: 'd:\\\\code\\\\PriceForecast\\\\logs\\\\pricepredict.log' -> 'd:\\\\code\\\\PriceForecast\\\\logs\\\\pricepredict.log.1'\n",
-      "Call stack:\n",
-      "  File \"<frozen runpy>\", line 198, in _run_module_as_main\n",
-      "  File \"<frozen runpy>\", line 88, in _run_code\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel_launcher.py\", line 17, in <module>\n",
-      "    app.launch_new_instance()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\traitlets\\config\\application.py\", line 992, in launch_instance\n",
-      "    app.start()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 701, in start\n",
-      "    self.io_loop.start()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 195, in start\n",
-      "    self.asyncio_loop.run_forever()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\asyncio\\windows_events.py\", line 321, in run_forever\n",
-      "    super().run_forever()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\asyncio\\base_events.py\", line 607, in run_forever\n",
-      "    self._run_once()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\asyncio\\base_events.py\", line 1922, in _run_once\n",
-      "    handle._run()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\asyncio\\events.py\", line 80, in _run\n",
-      "    self._context.run(self._callback, *self._args)\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in dispatch_queue\n",
-      "    await self.process_one()\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 523, in process_one\n",
-      "    await dispatch(*args)\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 429, in dispatch_shell\n",
-      "    await result\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 767, in execute_request\n",
-      "    reply_content = await reply_content\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 429, in do_execute\n",
-      "    res = shell.run_cell(\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n",
-      "    return super().run_cell(*args, **kwargs)\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3051, in run_cell\n",
-      "    result = self._run_cell(\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3106, in _run_cell\n",
-      "    result = runner(coro)\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n",
-      "    coro.send(None)\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3311, in run_cell_async\n",
-      "    has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3493, in run_ast_nodes\n",
-      "    if await self.run_code(code, result, async_=asy):\n",
-      "  File \"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3553, in run_code\n",
-      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
-      "  File \"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10336\\2387189848.py\", line 6, in <module>\n",
-      "    logger.info(f\"warning_data:{warning_data}\" )\n",
-      "Message: \"warning_data:{'funcModule': '原油特征停更预警', 'funcOperation': '原油特征停更预警', 'data': {'WARNING_TYPE_NAME': '特征数据停更预警test', 'WARNING_CONTENT': '                          停更周期                           预警日期                         最后更新时间                           更新周期                                 特征名称\\\\n                          2218                     2018-10-27                     2018-10-24                            1.0                      西北欧即期Brent211利润\\\\n                          1023                     2022-02-03                     2022-01-31                            1.0                            W-B活跃合约价差\\\\n                          1023                     2022-02-03                     2022-01-31                            1.0                            B-W活跃合约价差\\\\n                           734                     2022-11-19                     2022-11-16                            1.0                        Brent/迪拜原油升贴水\\\\n                           686                     2023-01-06                     2023-01-03                            1.0 PVMDubaiEFS/SwapvsICEFirstMonthBrent\\\\n                           446                     2023-09-03                     2023-08-31                            1.0                   美国RBOB期货2309月份合约价格\\\\n                           293                     2024-02-03                     2024-01-31                            1.0                      美国墨西哥湾87#汽油现货价格\\\\n                           265                     2024-03-02                     2024-02-28                            1.0                             Ebob连1合约\\\\n                           223                     2024-04-13                     2024-04-10                            1.0                              中国航班执行数\\\\n                            91                     2024-08-23                     2024-08-20                            1.0                         Kpler-海运在途原油\\\\n                            86                     2023-04-15                     2023-03-31                            7.0                  原油：开工率：东北地区：独立炼厂（周）\\\\n                            68                     2023-08-19                     2023-08-04                            7.0                        原油：港口库存：中国（周）\\\\n                            30                     2024-05-13                     2024-04-28                            7.0                        中东-巴林周度原油海运出口\\\\n                            26                     2024-06-08                     2024-05-24                            7.0                    原油：山东港口：库存：新口径（周）\\\\n                            26                     2024-06-08                     2024-05-24                            7.0             原油：山东港口：库存：新口径（周）超季节性/3年\\\\n                            23                     2024-06-29                     2024-06-14                            7.0               美国:炼油厂的投入与使用情况:开工率:当周值\\\\n                            23                     2024-06-29                     2024-06-14                            7.0         美国:东海岸地区:炼油厂的投入与使用情况:开工率:当周值\\\\n                            23                     2024-06-29                     2024-06-14                            7.0         美国:中西部地区:炼油厂的投入与使用情况:开工率:当周值\\\\n                            23                     2024-06-29                     2024-06-14                            7.0         美国:洛基山地区:炼油厂的投入与使用情况:开工率:当周值\\\\n                            23                     2024-06-29                     2024-06-14                            7.0        美国:墨西哥湾沿岸:炼油厂的投入与使用情况:开工率:当周值\\\\n                            23                     2024-06-29                     2024-06-14                            7.0         美国:西海岸地区:炼油厂的投入与使用情况:开工率:当周值\\\\n                            22                     2023-10-09                     2023-08-27                           21.0                        中东-也门周度原油海运出口\\\\n                            20                     2024-11-02                     2024-10-30                            1.0                    EFSc202:30收盘价滞后1天\\\\n                            20                     2024-11-02                     2024-10-30                            1.0                    EFSc102:30收盘价滞后1天\\\\n                            20                     2024-11-02                     2024-10-30                            1.0                  Brentc202:30收盘价滞后1天\\\\n                            20                     2024-11-02                     2024-10-30                            1.0                  Brentc102:30收盘价滞后1天\\\\n                            19                     2024-11-03                     2024-10-31                            1.0                        WTIc2-c3(结算B)\\\\n                            19                     2024-11-03                     2024-10-31                            1.0                      Brentc2-c3(结算B)\\\\n                            18                     2024-11-04                     2024-11-01                            1.0                               欧洲汽油裂解\\\\n                            18                     2024-08-05                     2024-07-21                            7.0                       全球原油库存（剔除富查伊拉）\\\\n                            13                     2024-09-09                     2024-08-25                            7.0                    Kpler-美国原油净进口（周度）\\\\n                             8                     2024-10-12                     2024-09-27                            7.0                          美国墨西哥湾原油钻机数\\\\n                             7                     2024-10-19                     2024-10-04                            7.0                             美国汽油产量占比\\\\n                             7                     2024-10-19                     2024-10-04                            7.0                             美国柴油产量占比\\\\n                             7                     2024-11-15                     2024-11-12                            1.0                     DFL(Dated-主力布伦特）\\\\n                             7                     2024-11-15                     2024-11-12                            1.0                   DFL(即期布伦特-布伦特期货C1）\\\\n                             6                     2024-11-16                     2024-11-13                            1.0                            国内马瑞与SC比价\\\\n                             6                     2024-11-16                     2024-11-13                            1.0                      丙烷CFR华南/布伦特原油比价\\\\n                             6                     2024-11-16                     2024-11-13                            1.0                       马瑞原油：现货价：山东（日）\\\\n                             6                     2024-11-16                     2024-11-13                            1.0                 马瑞原油：重质：DES价：委内瑞拉（日）\\\\n                             6                     2024-11-16                     2024-11-13                            1.0                       SC期货库存1000天百分位\\\\n                             6                     2024-11-16                     2024-11-13                            1.0                          SC/ZC（活跃合约）\\\\n                             6                     2024-11-16                     2024-11-13                            1.0                            SC仓单（百万桶）\\\\n                             6                     2024-11-16                     2024-11-13                            1.0                       SC期货库存10期滚动标准差\\\\n                             6                     2024-11-16                     2024-11-13                            1.0                SCc2夜盘-Brentc1(结算)*汇率\\\\n                             6                     2024-11-16                     2024-11-13                            1.0                SCc1夜盘-Brentc1(结算)*汇率\\\\n                             6                     2024-11-16                     2024-11-13                            1.0                          BU-SC（活跃合约）\\\\n                             6                     2024-11-16                     2024-11-13                            1.0                       Brentc2-c3(结算)\\\\n                             6                     2024-11-16                     2024-11-13                            1.0                SCc2夜盘-Brentc2(结算)*汇率\\\\n                             6                     2024-11-16                     2024-11-13                            1.0                               SC内外价差\\\\n                             5                     2024-11-17                     2024-11-14                            1.0                           VLCC:中东-中国\\\\n                             5                     2024-11-17                     2024-11-14                            1.0                             WTI连24合约\\\\n                             5                     2024-11-17                     2024-11-14                            1.0                    Suezmax：阿拉伯海湾-地中海\\\\n                             5                     2024-11-17                     2024-11-14                            1.0                          ICEBrentc14\\\\n                             5                     2024-11-17                     2024-11-14                            1.0             OMAN原油：中质：远期现货DES价：山东（日）\\\\n                             5                     2024-11-17                     2024-11-14                            1.0                             Brent-TI\\\\n                             5                     2024-11-17                     2024-11-14                            1.0                        美国MARS贴水（WTI）\\\\n                             5                     2024-11-17                     2024-11-14                            1.0                    Brent现货价格-Brent连3\\\\n                             5                     2024-11-17                     2024-11-14                            1.0                    lmcads03lmecomdty\\\\n                             5                     2024-11-17                     2024-11-14                            1.0                      布伦特现货价格-Brent连2\\\\n                             5                     2024-11-17                     2024-11-14                            1.0                         美国MARS原油现货价格\\\\n                             5                     2024-11-17                     2024-11-14                            1.0                            LLS原油现货价格\\\\n                             5                     2024-11-17                     2024-11-14                            1.0                          Brent原油现货价格\\\\n                             5                     2024-11-17                     2024-11-14                            1.0                     WTI原油现货价格Cushing\\\\n                             5                     2024-11-17                     2024-11-14                            1.0                             欧洲柴油现货价格\\\\n                             5                     2024-11-17                     2024-11-14                            1.0                        Midland原油现货价格\\\\n                             5                     2024-11-17                     2024-11-14                            1.0                           美湾低硫柴油现货价格\\\\n                             5                     2024-11-17                     2024-11-14                            1.0                     美国MEH轻原油现货价格（美湾）\\\\n                             5                     2024-11-17                     2024-11-14                            1.0                    Brent现货价格-Brent连4\\\\n                             5                     2024-11-17                     2024-11-14                            1.0                                  DFL\\\\n                             5                     2024-11-17                     2024-11-14                            1.0                      WTI原油Cushing-C1\\\\n                             5                     2024-11-17                     2024-11-14                            1.0                          LLS-Brent价差\\\\n                             3                     2024-11-16                     2024-11-01                            7.0                           美国取暖用油库存同差', 'WARNING_DATE': '2024-11-19 11:14:08'}}\"\n",
-      "Arguments: ()\n",
-      "warning_data:{'funcModule': '原油特征停更预警', 'funcOperation': '原油特征停更预警', 'data': {'WARNING_TYPE_NAME': '特征数据停更预警test', 'WARNING_CONTENT': '                          停更周期                           预警日期                         最后更新时间                           更新周期                                 特征名称\\n                          2218                     2018-10-27                     2018-10-24                            1.0                      西北欧即期Brent211利润\\n                          1023                     2022-02-03                     2022-01-31                            1.0                            W-B活跃合约价差\\n                          1023                     2022-02-03                     2022-01-31                            1.0                            B-W活跃合约价差\\n                           734                     2022-11-19                     2022-11-16                            1.0                        Brent/迪拜原油升贴水\\n                           686                     2023-01-06                     2023-01-03                            1.0 PVMDubaiEFS/SwapvsICEFirstMonthBrent\\n                           446                     2023-09-03                     2023-08-31                            1.0                   美国RBOB期货2309月份合约价格\\n                           293                     2024-02-03                     2024-01-31                            1.0                      美国墨西哥湾87#汽油现货价格\\n                           265                     2024-03-02                     2024-02-28                            1.0                             Ebob连1合约\\n                           223                     2024-04-13                     2024-04-10                            1.0                              中国航班执行数\\n                            91                     2024-08-23                     2024-08-20                            1.0                         Kpler-海运在途原油\\n                            86                     2023-04-15                     2023-03-31                            7.0                  原油：开工率：东北地区：独立炼厂（周）\\n                            68                     2023-08-19                     2023-08-04                            7.0                        原油：港口库存：中国（周）\\n                            30                     2024-05-13                     2024-04-28                            7.0                        中东-巴林周度原油海运出口\\n                            26                     2024-06-08                     2024-05-24                            7.0                    原油：山东港口：库存：新口径（周）\\n                            26                     2024-06-08                     2024-05-24                            7.0             原油：山东港口：库存：新口径（周）超季节性/3年\\n                            23                     2024-06-29                     2024-06-14                            7.0               美国:炼油厂的投入与使用情况:开工率:当周值\\n                            23                     2024-06-29                     2024-06-14                            7.0         美国:东海岸地区:炼油厂的投入与使用情况:开工率:当周值\\n                            23                     2024-06-29                     2024-06-14                            7.0         美国:中西部地区:炼油厂的投入与使用情况:开工率:当周值\\n                            23                     2024-06-29                     2024-06-14                            7.0         美国:洛基山地区:炼油厂的投入与使用情况:开工率:当周值\\n                            23                     2024-06-29                     2024-06-14                            7.0        美国:墨西哥湾沿岸:炼油厂的投入与使用情况:开工率:当周值\\n                            23                     2024-06-29                     2024-06-14                            7.0         美国:西海岸地区:炼油厂的投入与使用情况:开工率:当周值\\n                            22                     2023-10-09                     2023-08-27                           21.0                        中东-也门周度原油海运出口\\n                            20                     2024-11-02                     2024-10-30                            1.0                    EFSc202:30收盘价滞后1天\\n                            20                     2024-11-02                     2024-10-30                            1.0                    EFSc102:30收盘价滞后1天\\n                            20                     2024-11-02                     2024-10-30                            1.0                  Brentc202:30收盘价滞后1天\\n                            20                     2024-11-02                     2024-10-30                            1.0                  Brentc102:30收盘价滞后1天\\n                            19                     2024-11-03                     2024-10-31                            1.0                        WTIc2-c3(结算B)\\n                            19                     2024-11-03                     2024-10-31                            1.0                      Brentc2-c3(结算B)\\n                            18                     2024-11-04                     2024-11-01                            1.0                               欧洲汽油裂解\\n                            18                     2024-08-05                     2024-07-21                            7.0                       全球原油库存（剔除富查伊拉）\\n                            13                     2024-09-09                     2024-08-25                            7.0                    Kpler-美国原油净进口（周度）\\n                             8                     2024-10-12                     2024-09-27                            7.0                          美国墨西哥湾原油钻机数\\n                             7                     2024-10-19                     2024-10-04                            7.0                             美国汽油产量占比\\n                             7                     2024-10-19                     2024-10-04                            7.0                             美国柴油产量占比\\n                             7                     2024-11-15                     2024-11-12                            1.0                     DFL(Dated-主力布伦特）\\n                             7                     2024-11-15                     2024-11-12                            1.0                   DFL(即期布伦特-布伦特期货C1）\\n                             6                     2024-11-16                     2024-11-13                            1.0                            国内马瑞与SC比价\\n                             6                     2024-11-16                     2024-11-13                            1.0                      丙烷CFR华南/布伦特原油比价\\n                             6                     2024-11-16                     2024-11-13                            1.0                       马瑞原油：现货价：山东（日）\\n                             6                     2024-11-16                     2024-11-13                            1.0                 马瑞原油：重质：DES价：委内瑞拉（日）\\n                             6                     2024-11-16                     2024-11-13                            1.0                       SC期货库存1000天百分位\\n                             6                     2024-11-16                     2024-11-13                            1.0                          SC/ZC（活跃合约）\\n                             6                     2024-11-16                     2024-11-13                            1.0                            SC仓单（百万桶）\\n                             6                     2024-11-16                     2024-11-13                            1.0                       SC期货库存10期滚动标准差\\n                             6                     2024-11-16                     2024-11-13                            1.0                SCc2夜盘-Brentc1(结算)*汇率\\n                             6                     2024-11-16                     2024-11-13                            1.0                SCc1夜盘-Brentc1(结算)*汇率\\n                             6                     2024-11-16                     2024-11-13                            1.0                          BU-SC（活跃合约）\\n                             6                     2024-11-16                     2024-11-13                            1.0                       Brentc2-c3(结算)\\n                             6                     2024-11-16                     2024-11-13                            1.0                SCc2夜盘-Brentc2(结算)*汇率\\n                             6                     2024-11-16                     2024-11-13                            1.0                               SC内外价差\\n                             5                     2024-11-17                     2024-11-14                            1.0                           VLCC:中东-中国\\n                             5                     2024-11-17                     2024-11-14                            1.0                             WTI连24合约\\n                             5                     2024-11-17                     2024-11-14                            1.0                    Suezmax：阿拉伯海湾-地中海\\n                             5                     2024-11-17                     2024-11-14                            1.0                          ICEBrentc14\\n                             5                     2024-11-17                     2024-11-14                            1.0             OMAN原油：中质：远期现货DES价：山东（日）\\n                             5                     2024-11-17                     2024-11-14                            1.0                             Brent-TI\\n                             5                     2024-11-17                     2024-11-14                            1.0                        美国MARS贴水（WTI）\\n                             5                     2024-11-17                     2024-11-14                            1.0                    Brent现货价格-Brent连3\\n                             5                     2024-11-17                     2024-11-14                            1.0                    lmcads03lmecomdty\\n                             5                     2024-11-17                     2024-11-14                            1.0                      布伦特现货价格-Brent连2\\n                             5                     2024-11-17                     2024-11-14                            1.0                         美国MARS原油现货价格\\n                             5                     2024-11-17                     2024-11-14                            1.0                            LLS原油现货价格\\n                             5                     2024-11-17                     2024-11-14                            1.0                          Brent原油现货价格\\n                             5                     2024-11-17                     2024-11-14                            1.0                     WTI原油现货价格Cushing\\n                             5                     2024-11-17                     2024-11-14                            1.0                             欧洲柴油现货价格\\n                             5                     2024-11-17                     2024-11-14                            1.0                        Midland原油现货价格\\n                             5                     2024-11-17                     2024-11-14                            1.0                           美湾低硫柴油现货价格\\n                             5                     2024-11-17                     2024-11-14                            1.0                     美国MEH轻原油现货价格（美湾）\\n                             5                     2024-11-17                     2024-11-14                            1.0                    Brent现货价格-Brent连4\\n                             5                     2024-11-17                     2024-11-14                            1.0                                  DFL\\n                             5                     2024-11-17                     2024-11-14                            1.0                      WTI原油Cushing-C1\\n                             5                     2024-11-17                     2024-11-14                            1.0                          LLS-Brent价差\\n                             3                     2024-11-16                     2024-11-01                            7.0                           美国取暖用油库存同差', 'WARNING_DATE': '2024-11-19 11:14:08'}}\n"
+      "token:eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfdGVzdCIsInRoIjoiOGE0NTc3ZGJkOTE5Njc1NzU4ZDU3OTk5YTFlODkxZmUiLCJsdCI6ImFwaSIsImlzcyI6IiIsInRtIjoiUEMiLCJleHAiOjE3MzIxMDcwNDUsImp0aSI6ImM0Njk5ZGY3YWYwMTQ1Y2I4NWUzYjJhZTg2ZjMzYzRiIn0.EuDSlyPRNA3ULUyNHCKpj2nZCCyRqpOY72A_qkoSkJs\n",
+      "warning_data:{'funcModule': '原油特征停更预警', 'funcOperation': '原油特征停更预警', 'data': {'WARNING_TYPE_NAME': '特征数据停更预警', 'WARNING_CONTENT': '                          停更周期                           预警日期                         最后更新时间                           更新周期                                 特征名称\\n                          2218                     2018-10-27                     2018-10-24                            1.0                      西北欧即期Brent211利润\\n                          1023                     2022-02-03                     2022-01-31                            1.0                            B-W活跃合约价差\\n                          1023                     2022-02-03                     2022-01-31                            1.0                            W-B活跃合约价差\\n                           734                     2022-11-19                     2022-11-16                            1.0                        Brent/迪拜原油升贴水\\n                           686                     2023-01-06                     2023-01-03                            1.0 PVMDubaiEFS/SwapvsICEFirstMonthBrent\\n                           446                     2023-09-03                     2023-08-31                            1.0                   美国RBOB期货2309月份合约价格\\n                           293                     2024-02-03                     2024-01-31                            1.0                      美国墨西哥湾87#汽油现货价格\\n                           265                     2024-03-02                     2024-02-28                            1.0                             Ebob连1合约\\n                           223                     2024-04-13                     2024-04-10                            1.0                              中国航班执行数\\n                            91                     2024-08-23                     2024-08-20                            1.0                         Kpler-海运在途原油\\n                            86                     2023-04-15                     2023-03-31                            7.0                  原油：开工率：东北地区：独立炼厂（周）\\n                            68                     2023-08-19                     2023-08-04                            7.0                        原油：港口库存：中国（周）\\n                            30                     2024-05-13                     2024-04-28                            7.0                        中东-巴林周度原油海运出口\\n                            26                     2024-06-08                     2024-05-24                            7.0                    原油：山东港口：库存：新口径（周）\\n                            26                     2024-06-08                     2024-05-24                            7.0             原油：山东港口：库存：新口径（周）超季节性/3年\\n                            23                     2024-06-29                     2024-06-14                            7.0               美国:炼油厂的投入与使用情况:开工率:当周值\\n                            23                     2024-06-29                     2024-06-14                            7.0        美国:墨西哥湾沿岸:炼油厂的投入与使用情况:开工率:当周值\\n                            23                     2024-06-29                     2024-06-14                            7.0         美国:洛基山地区:炼油厂的投入与使用情况:开工率:当周值\\n                            23                     2024-06-29                     2024-06-14                            7.0         美国:西海岸地区:炼油厂的投入与使用情况:开工率:当周值\\n                            23                     2024-06-29                     2024-06-14                            7.0         美国:东海岸地区:炼油厂的投入与使用情况:开工率:当周值\\n                            23                     2024-06-29                     2024-06-14                            7.0         美国:中西部地区:炼油厂的投入与使用情况:开工率:当周值\\n                            22                     2023-10-09                     2023-08-27                           21.0                        中东-也门周度原油海运出口\\n                            20                     2024-11-02                     2024-10-30                            1.0                  Brentc102:30收盘价滞后1天\\n                            20                     2024-11-02                     2024-10-30                            1.0                  Brentc202:30收盘价滞后1天\\n                            20                     2024-11-02                     2024-10-30                            1.0                    EFSc102:30收盘价滞后1天\\n                            20                     2024-11-02                     2024-10-30                            1.0                    EFSc202:30收盘价滞后1天\\n                            19                     2024-11-03                     2024-10-31                            1.0                        WTIc2-c3(结算B)\\n                            19                     2024-11-03                     2024-10-31                            1.0                      Brentc2-c3(结算B)\\n                            18                     2024-08-05                     2024-07-21                            7.0                       全球原油库存（剔除富查伊拉）\\n                            13                     2024-09-09                     2024-08-25                            7.0                    Kpler-美国原油净进口（周度）\\n                             8                     2024-10-12                     2024-09-27                            7.0                          美国墨西哥湾原油钻机数\\n                             7                     2024-10-19                     2024-10-04                            7.0                             美国柴油产量占比\\n                             7                     2024-10-19                     2024-10-04                            7.0                             美国汽油产量占比\\n                             6                     2024-11-16                     2024-11-13                            1.0                 马瑞原油：重质：DES价：委内瑞拉（日）\\n                             6                     2024-11-16                     2024-11-13                            1.0                      丙烷CFR华南/布伦特原油比价\\n                             6                     2024-11-16                     2024-11-13                            1.0                            国内马瑞与SC比价\\n                             6                     2024-11-16                     2024-11-13                            1.0                       马瑞原油：现货价：山东（日）\\n                             5                     2024-11-17                     2024-11-14                            1.0             OMAN原油：中质：远期现货DES价：山东（日）', 'WARNING_DATE': '2024-11-20 10:50:47'}}\n"
      ]
     },
     {
@@ -362,7 +194,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
diff --git a/聚烯烃预测值绘图调试.ipynb b/聚烯烃预测值绘图调试.ipynb
index d516188..f9a0c4f 100644
--- a/聚烯烃预测值绘图调试.ipynb
+++ b/聚烯烃预测值绘图调试.ipynb
@@ -18,53 +18,1643 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "从eta获取数据...\n",
-      "从eta获取数据...\n"
+      "读取本地数据：juxitingdataset\\PP指标数据.xlsx\n",
+      "读取本地数据：juxitingdataset\\PP指标数据.xlsx\n",
+      "getdata接收：juxitingdataset\\PP指标数据.xlsx date \n",
+      "getdata接收：juxitingdataset\\PP指标数据.xlsx date \n",
+      "删除两月不更新特征前数据量：(2516, 354)\n",
+      "删除两月不更新特征前数据量：(2516, 354)\n",
+      "删除两月不更新特征后数据量：(2516, 314)\n",
+      "删除两月不更新特征后数据量：(2516, 314)\n",
+      "删除预测列为空值的行后数据量：(772, 314)\n",
+      "删除预测列为空值的行后数据量：(772, 314)\n",
+      "删除全为空值的列后数据量：(772, 314)\n",
+      "删除全为空值的列后数据量：(772, 314)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  values.dropna(inplace=True,axis=0)\n",
+      "                  日度(218)         周度(94)                 84天(1)\n",
+      "0    PP主力收盘价拟合残差/丙烷 CP M1      PE注塑开工率/周  中国：华东地区：市场平均价：BOPP厚光膜\n",
+      "1         华南聚丙烯基差（折盘面收盘价）  PP：看跌比例：中国（周）                       \n",
+      "2         华北聚丙烯基差（折盘面收盘价）  PP：看平比例：中国（周）                       \n",
+      "3         华东聚丙烯基差（折盘面收盘价）  PP：看升比例：中国（周）                       \n",
+      "4                 煤制聚丙烯利润     PP看空情绪指数环差                       \n",
+      "..                    ...            ...                    ...\n",
+      "213               PE期货收盘价                                      \n",
+      "214               PP连续-1月                                      \n",
+      "215               PP连续-5月                                      \n",
+      "216               PP连续-9月                                      \n",
+      "217                     y                                      \n",
+      "\n",
+      "[218 rows x 3 columns]\n",
+      "                  日度(218)         周度(94)                 84天(1)\n",
+      "0    PP主力收盘价拟合残差/丙烷 CP M1      PE注塑开工率/周  中国：华东地区：市场平均价：BOPP厚光膜\n",
+      "1         华南聚丙烯基差（折盘面收盘价）  PP：看跌比例：中国（周）                       \n",
+      "2         华北聚丙烯基差（折盘面收盘价）  PP：看平比例：中国（周）                       \n",
+      "3         华东聚丙烯基差（折盘面收盘价）  PP：看升比例：中国（周）                       \n",
+      "4                 煤制聚丙烯利润     PP看空情绪指数环差                       \n",
+      "..                    ...            ...                    ...\n",
+      "213               PE期货收盘价                                      \n",
+      "214               PP连续-1月                                      \n",
+      "215               PP连续-5月                                      \n",
+      "216               PP连续-9月                                      \n",
+      "217                     y                                      \n",
+      "\n",
+      "[218 rows x 3 columns]\n",
+      "特征信息：总共有311个,日度(218),周度(94),84天(1), 详看 附1、特征列表\n",
+      "        数据特征工程：\n",
+      "    1. 数据日期排序，新日期在最后\n",
+      "    2. 删除空列，特征数据列没有值，就删除\n",
+      "    3. 删除近两月不再更新值的指标\n",
+      "    4. 非日度数据填充为日度数据，填充规则：\n",
+      "    -- 向后填充，举例：假设周五出现一个周度指标数据，那么在这之前的数据用上周五的数据\n",
+      "    -- 向前填充，举例：采集数据开始日期为2018年1月1日，那么周度数据可能是2018年1月3日，那么3日的数据向前填充，使1日2日都有数值\n",
+      "        数据特征相关性分析：\n",
+      "    \n",
+      "特征信息：总共有311个,日度(218),周度(94),84天(1), 详看 附1、特征列表\n",
+      "        数据特征工程：\n",
+      "    1. 数据日期排序，新日期在最后\n",
+      "    2. 删除空列，特征数据列没有值，就删除\n",
+      "    3. 删除近两月不再更新值的指标\n",
+      "    4. 非日度数据填充为日度数据，填充规则：\n",
+      "    -- 向后填充，举例：假设周五出现一个周度指标数据，那么在这之前的数据用上周五的数据\n",
+      "    -- 向前填充，举例：采集数据开始日期为2018年1月1日，那么周度数据可能是2018年1月3日，那么3日的数据向前填充，使1日2日都有数值\n",
+      "        数据特征相关性分析：\n",
+      "    \n",
+      "********************************************************************************************************************************************************************************************************\n",
+      "********************************************************************************************************************************************************************************************************\n"
      ]
     },
     {
-     "ename": "Exception",
-     "evalue": "(\"请求失败,请确认是否为内网环境: HTTPConnectionPool(host='10.189.2.78', port=8108): Max retries exceeded with url: /v1/edb/classify/list?ClassifyType= (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x0000028116365890>, 'Connection to 10.189.2.78 timed out. (connect timeout=None)'))\", '\\x1b[0m')",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTimeoutError\u001b[0m                              Traceback (most recent call last)",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\connection.py:203\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    202\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 203\u001b[0m     sock \u001b[38;5;241m=\u001b[39m connection\u001b[38;5;241m.\u001b[39mcreate_connection(\n\u001b[0;32m    204\u001b[0m         (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dns_host, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mport),\n\u001b[0;32m    205\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout,\n\u001b[0;32m    206\u001b[0m         source_address\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msource_address,\n\u001b[0;32m    207\u001b[0m         socket_options\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msocket_options,\n\u001b[0;32m    208\u001b[0m     )\n\u001b[0;32m    209\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m socket\u001b[38;5;241m.\u001b[39mgaierror \u001b[38;5;28;01mas\u001b[39;00m e:\n",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\util\\connection.py:85\u001b[0m, in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     84\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 85\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m err\n\u001b[0;32m     86\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     87\u001b[0m     \u001b[38;5;66;03m# Break explicitly a reference cycle\u001b[39;00m\n",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\util\\connection.py:73\u001b[0m, in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     72\u001b[0m     sock\u001b[38;5;241m.\u001b[39mbind(source_address)\n\u001b[1;32m---> 73\u001b[0m sock\u001b[38;5;241m.\u001b[39mconnect(sa)\n\u001b[0;32m     74\u001b[0m \u001b[38;5;66;03m# Break explicitly a reference cycle\u001b[39;00m\n",
-      "\u001b[1;31mTimeoutError\u001b[0m: [WinError 10060] 由于连接方在一段时间后没有正确答复或连接的主机没有反应，连接尝试失败。",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[1;31mConnectTimeoutError\u001b[0m                       Traceback (most recent call last)",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\connectionpool.py:791\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[0;32m    790\u001b[0m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[1;32m--> 791\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_request(\n\u001b[0;32m    792\u001b[0m     conn,\n\u001b[0;32m    793\u001b[0m     method,\n\u001b[0;32m    794\u001b[0m     url,\n\u001b[0;32m    795\u001b[0m     timeout\u001b[38;5;241m=\u001b[39mtimeout_obj,\n\u001b[0;32m    796\u001b[0m     body\u001b[38;5;241m=\u001b[39mbody,\n\u001b[0;32m    797\u001b[0m     headers\u001b[38;5;241m=\u001b[39mheaders,\n\u001b[0;32m    798\u001b[0m     chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[0;32m    799\u001b[0m     retries\u001b[38;5;241m=\u001b[39mretries,\n\u001b[0;32m    800\u001b[0m     response_conn\u001b[38;5;241m=\u001b[39mresponse_conn,\n\u001b[0;32m    801\u001b[0m     preload_content\u001b[38;5;241m=\u001b[39mpreload_content,\n\u001b[0;32m    802\u001b[0m     decode_content\u001b[38;5;241m=\u001b[39mdecode_content,\n\u001b[0;32m    803\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mresponse_kw,\n\u001b[0;32m    804\u001b[0m )\n\u001b[0;32m    806\u001b[0m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\connectionpool.py:497\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[1;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[0;32m    496\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 497\u001b[0m     conn\u001b[38;5;241m.\u001b[39mrequest(\n\u001b[0;32m    498\u001b[0m         method,\n\u001b[0;32m    499\u001b[0m         url,\n\u001b[0;32m    500\u001b[0m         body\u001b[38;5;241m=\u001b[39mbody,\n\u001b[0;32m    501\u001b[0m         headers\u001b[38;5;241m=\u001b[39mheaders,\n\u001b[0;32m    502\u001b[0m         chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[0;32m    503\u001b[0m         preload_content\u001b[38;5;241m=\u001b[39mpreload_content,\n\u001b[0;32m    504\u001b[0m         decode_content\u001b[38;5;241m=\u001b[39mdecode_content,\n\u001b[0;32m    505\u001b[0m         enforce_content_length\u001b[38;5;241m=\u001b[39menforce_content_length,\n\u001b[0;32m    506\u001b[0m     )\n\u001b[0;32m    508\u001b[0m \u001b[38;5;66;03m# We are swallowing BrokenPipeError (errno.EPIPE) since the server is\u001b[39;00m\n\u001b[0;32m    509\u001b[0m \u001b[38;5;66;03m# legitimately able to close the connection after sending a valid response.\u001b[39;00m\n\u001b[0;32m    510\u001b[0m \u001b[38;5;66;03m# With this behaviour, the received response is still readable.\u001b[39;00m\n",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\connection.py:395\u001b[0m, in \u001b[0;36mHTTPConnection.request\u001b[1;34m(self, method, url, body, headers, chunked, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[0;32m    394\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mputheader(header, value)\n\u001b[1;32m--> 395\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mendheaders()\n\u001b[0;32m    397\u001b[0m \u001b[38;5;66;03m# If we're given a body we start sending that in chunks.\u001b[39;00m\n",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\http\\client.py:1289\u001b[0m, in \u001b[0;36mHTTPConnection.endheaders\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1288\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CannotSendHeader()\n\u001b[1;32m-> 1289\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_send_output(message_body, encode_chunked\u001b[38;5;241m=\u001b[39mencode_chunked)\n",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\http\\client.py:1048\u001b[0m, in \u001b[0;36mHTTPConnection._send_output\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1047\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_buffer[:]\n\u001b[1;32m-> 1048\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend(msg)\n\u001b[0;32m   1050\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m message_body \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1051\u001b[0m \n\u001b[0;32m   1052\u001b[0m     \u001b[38;5;66;03m# create a consistent interface to message_body\u001b[39;00m\n",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\http\\client.py:986\u001b[0m, in \u001b[0;36mHTTPConnection.send\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    985\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_open:\n\u001b[1;32m--> 986\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconnect()\n\u001b[0;32m    987\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\connection.py:243\u001b[0m, in \u001b[0;36mHTTPConnection.connect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    242\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconnect\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 243\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_new_conn()\n\u001b[0;32m    244\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tunnel_host:\n\u001b[0;32m    245\u001b[0m         \u001b[38;5;66;03m# If we're tunneling it means we're connected to our proxy.\u001b[39;00m\n",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\connection.py:212\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    211\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SocketTimeout \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m--> 212\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ConnectTimeoutError(\n\u001b[0;32m    213\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    214\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m timed out. (connect timeout=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    215\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[0;32m    217\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
-      "\u001b[1;31mConnectTimeoutError\u001b[0m: (<urllib3.connection.HTTPConnection object at 0x0000028116365890>, 'Connection to 10.189.2.78 timed out. (connect timeout=None)')",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[1;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\requests\\adapters.py:486\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    485\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 486\u001b[0m     resp \u001b[38;5;241m=\u001b[39m conn\u001b[38;5;241m.\u001b[39murlopen(\n\u001b[0;32m    487\u001b[0m         method\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mmethod,\n\u001b[0;32m    488\u001b[0m         url\u001b[38;5;241m=\u001b[39murl,\n\u001b[0;32m    489\u001b[0m         body\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mbody,\n\u001b[0;32m    490\u001b[0m         headers\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mheaders,\n\u001b[0;32m    491\u001b[0m         redirect\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    492\u001b[0m         assert_same_host\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    493\u001b[0m         preload_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    494\u001b[0m         decode_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    495\u001b[0m         retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_retries,\n\u001b[0;32m    496\u001b[0m         timeout\u001b[38;5;241m=\u001b[39mtimeout,\n\u001b[0;32m    497\u001b[0m         chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[0;32m    498\u001b[0m     )\n\u001b[0;32m    500\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\connectionpool.py:845\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[0;32m    843\u001b[0m     new_e \u001b[38;5;241m=\u001b[39m ProtocolError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection aborted.\u001b[39m\u001b[38;5;124m\"\u001b[39m, new_e)\n\u001b[1;32m--> 845\u001b[0m retries \u001b[38;5;241m=\u001b[39m retries\u001b[38;5;241m.\u001b[39mincrement(\n\u001b[0;32m    846\u001b[0m     method, url, error\u001b[38;5;241m=\u001b[39mnew_e, _pool\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m, _stacktrace\u001b[38;5;241m=\u001b[39msys\u001b[38;5;241m.\u001b[39mexc_info()[\u001b[38;5;241m2\u001b[39m]\n\u001b[0;32m    847\u001b[0m )\n\u001b[0;32m    848\u001b[0m retries\u001b[38;5;241m.\u001b[39msleep()\n",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\util\\retry.py:515\u001b[0m, in \u001b[0;36mRetry.increment\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m    514\u001b[0m     reason \u001b[38;5;241m=\u001b[39m error \u001b[38;5;129;01mor\u001b[39;00m ResponseError(cause)\n\u001b[1;32m--> 515\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m MaxRetryError(_pool, url, reason) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mreason\u001b[39;00m  \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[0;32m    517\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIncremented Retry for (url=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m): \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, url, new_retry)\n",
-      "\u001b[1;31mMaxRetryError\u001b[0m: HTTPConnectionPool(host='10.189.2.78', port=8108): Max retries exceeded with url: /v1/edb/classify/list?ClassifyType= (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x0000028116365890>, 'Connection to 10.189.2.78 timed out. (connect timeout=None)'))",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[1;31mConnectTimeout\u001b[0m                            Traceback (most recent call last)",
-      "File \u001b[1;32md:\\code\\PriceForecast\\lib\\dataread.py:1273\u001b[0m, in \u001b[0;36mEtaReader.get_eta_api_pp_data\u001b[1;34m(self, data_set, dataset)\u001b[0m\n\u001b[0;32m   1271\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1272\u001b[0m     \u001b[38;5;66;03m# 发送GET请求  获取指标分类列表\u001b[39;00m\n\u001b[1;32m-> 1273\u001b[0m     response \u001b[38;5;241m=\u001b[39m requests\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclassifylisturl, headers\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mheaders)  \n\u001b[0;32m   1274\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m requests\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mRequestException \u001b[38;5;28;01mas\u001b[39;00m e:\n",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\requests\\api.py:73\u001b[0m, in \u001b[0;36mget\u001b[1;34m(url, params, **kwargs)\u001b[0m\n\u001b[0;32m     63\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a GET request.\u001b[39;00m\n\u001b[0;32m     64\u001b[0m \n\u001b[0;32m     65\u001b[0m \u001b[38;5;124;03m:param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     70\u001b[0m \u001b[38;5;124;03m:rtype: requests.Response\u001b[39;00m\n\u001b[0;32m     71\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m---> 73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m request(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mget\u001b[39m\u001b[38;5;124m\"\u001b[39m, url, params\u001b[38;5;241m=\u001b[39mparams, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\requests\\api.py:59\u001b[0m, in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m sessions\u001b[38;5;241m.\u001b[39mSession() \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[1;32m---> 59\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m session\u001b[38;5;241m.\u001b[39mrequest(method\u001b[38;5;241m=\u001b[39mmethod, url\u001b[38;5;241m=\u001b[39murl, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\requests\\sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[1;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend(prep, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msend_kwargs)\n\u001b[0;32m    591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\requests\\sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[1;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m adapter\u001b[38;5;241m.\u001b[39msend(request, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n",
-      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\requests\\adapters.py:507\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    506\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, NewConnectionError):\n\u001b[1;32m--> 507\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ConnectTimeout(e, request\u001b[38;5;241m=\u001b[39mrequest)\n\u001b[0;32m    509\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, ResponseError):\n",
-      "\u001b[1;31mConnectTimeout\u001b[0m: HTTPConnectionPool(host='10.189.2.78', port=8108): Max retries exceeded with url: /v1/edb/classify/list?ClassifyType= (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x0000028116365890>, 'Connection to 10.189.2.78 timed out. (connect timeout=None)'))",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[1], line 37\u001b[0m\n\u001b[0;32m     27\u001b[0m signature \u001b[38;5;241m=\u001b[39m BinanceAPI(APPID, SECRET)\n\u001b[0;32m     28\u001b[0m etadata \u001b[38;5;241m=\u001b[39m EtaReader(signature\u001b[38;5;241m=\u001b[39msignature,\n\u001b[0;32m     29\u001b[0m                             classifylisturl \u001b[38;5;241m=\u001b[39m classifylisturl,\n\u001b[0;32m     30\u001b[0m                             classifyidlisturl\u001b[38;5;241m=\u001b[39mclassifyidlisturl,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     35\u001b[0m                             edbbusinessurl\u001b[38;5;241m=\u001b[39medbbusinessurl,\n\u001b[0;32m     36\u001b[0m                             )\n\u001b[1;32m---> 37\u001b[0m df_zhibiaoshuju,df_zhibiaoliebiao \u001b[38;5;241m=\u001b[39m  etadata\u001b[38;5;241m.\u001b[39mget_eta_api_pp_data(data_set\u001b[38;5;241m=\u001b[39mdata_set,dataset\u001b[38;5;241m=\u001b[39mdataset)  \u001b[38;5;66;03m# 原始数据，未处理\u001b[39;00m\n\u001b[0;32m     39\u001b[0m \u001b[38;5;66;03m# 数据处理\u001b[39;00m\n\u001b[0;32m     40\u001b[0m df \u001b[38;5;241m=\u001b[39m datachuli_juxiting(df_zhibiaoshuju,df_zhibiaoliebiao,y \u001b[38;5;241m=\u001b[39m y,dataset\u001b[38;5;241m=\u001b[39mdataset,add_kdj\u001b[38;5;241m=\u001b[39madd_kdj,is_timefurture\u001b[38;5;241m=\u001b[39mis_timefurture,end_time\u001b[38;5;241m=\u001b[39mend_time) \n",
-      "File \u001b[1;32md:\\code\\PriceForecast\\lib\\dataread.py:1275\u001b[0m, in \u001b[0;36mEtaReader.get_eta_api_pp_data\u001b[1;34m(self, data_set, dataset)\u001b[0m\n\u001b[0;32m   1273\u001b[0m     response \u001b[38;5;241m=\u001b[39m requests\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclassifylisturl, headers\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mheaders)  \n\u001b[0;32m   1274\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m requests\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mRequestException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m-> 1275\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m请求失败,请确认是否为内网环境: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[0m\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1277\u001b[0m \u001b[38;5;66;03m# 检查响应状态码  \u001b[39;00m\n\u001b[0;32m   1278\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m200\u001b[39m: \n\u001b[0;32m   1279\u001b[0m     \u001b[38;5;66;03m# 获取成功， 处理响应内容\u001b[39;00m\n",
-      "\u001b[1;31mException\u001b[0m: (\"请求失败,请确认是否为内网环境: HTTPConnectionPool(host='10.189.2.78', port=8108): Max retries exceeded with url: /v1/edb/classify/list?ClassifyType= (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x0000028116365890>, 'Connection to 10.189.2.78 timed out. (connect timeout=None)'))\", '\\x1b[0m')"
-     ]
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x1000 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -125,7 +1715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "ae059224-976c-4839-b455-f81da7f25179",
    "metadata": {},
    "outputs": [],
@@ -134,25 +1724,25 @@
     "# 取第一行数据存储到数据库中\n",
     "first_row = df[['ds','y']].tail(1)\n",
     "# 将最新真实值保存到数据库\n",
-    "if not sqlitedb.check_table_exists('trueandpredict'):\n",
-    "    first_row.to_sql('trueandpredict',sqlitedb.connection,index=False)\n",
-    "else:\n",
-    "    for row in first_row.itertuples(index=False):\n",
-    "        row_dict = row._asdict()\n",
-    "        row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')\n",
-    "        check_query = sqlitedb.select_data('trueandpredict',where_condition = f\"ds = '{row.ds}'\")\n",
-    "        if len(check_query) > 0:\n",
-    "            set_clause = \", \".join([f\"{key} = '{value}'\" for key, value in row_dict.items()])\n",
-    "            sqlitedb.update_data('trueandpredict',set_clause,where_condition = f\"ds = '{row.ds}'\")\n",
-    "            continue\n",
-    "        sqlitedb.insert_data('trueandpredict',tuple(row_dict.values()),columns=row_dict.keys())\n",
+    "# if not sqlitedb.check_table_exists('trueandpredict'):\n",
+    "#     first_row.to_sql('trueandpredict',sqlitedb.connection,index=False)\n",
+    "# else:\n",
+    "#     for row in first_row.itertuples(index=False):\n",
+    "#         row_dict = row._asdict()\n",
+    "#         row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')\n",
+    "#         check_query = sqlitedb.select_data('trueandpredict',where_condition = f\"ds = '{row.ds}'\")\n",
+    "#         if len(check_query) > 0:\n",
+    "#             set_clause = \", \".join([f\"{key} = '{value}'\" for key, value in row_dict.items()])\n",
+    "#             sqlitedb.update_data('trueandpredict',set_clause,where_condition = f\"ds = '{row.ds}'\")\n",
+    "#             continue\n",
+    "#         sqlitedb.insert_data('trueandpredict',tuple(row_dict.values()),columns=row_dict.keys())\n",
     "\n",
     "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "abb597fc-c5f3-4d76-8099-5eff358cb634",
    "metadata": {},
    "outputs": [],
@@ -198,10 +1788,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "ade7026e-8cf2-405f-a2da-9e90f364adab",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "开始训练模型...\n",
+      "开始训练模型...\n"
+     ]
+    }
+   ],
    "source": [
     "if is_corr:\n",
     "    df = corr_feature(df=df)\n",
@@ -213,7 +1812,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "dfef57d8-36da-423b-bbe7-05a13e15f71b",
    "metadata": {},
    "outputs": [],
@@ -245,7 +1844,525 @@
    "execution_count": null,
    "id": "0e5b6f30-b7ca-4718-97a3-48b54156e07f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "模型训练完成\n",
+      "模型训练完成\n",
+      "训练数据绘图ing\n",
+      "训练数据绘图ing\n",
+      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_19344\\741737176.py:111: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  names_df['columns'] = names_df.apply(add_rote_column, axis=1)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NLinear-NHITS-误差比例            float32\n",
+      "RNN-NHITS-误差比例                float32\n",
+      "TiDE-NHITS-误差比例               float32\n",
+      "TSMixer-NHITS-误差比例            float32\n",
+      "PatchTST-NHITS-误差比例           float32\n",
+      "NHITS-NHITS-误差比例              float32\n",
+      "DilatedRNN-NHITS-误差比例         float32\n",
+      "DLinear-NHITS-误差比例            float32\n",
+      "GRU-NHITS-误差比例                float32\n",
+      "LSTM-NHITS-误差比例               float32\n",
+      "Informer-NHITS-误差比例           float32\n",
+      "DeepNPTS-NHITS-误差比例           float32\n",
+      "MLP-NHITS-误差比例                float32\n",
+      "TFT-NHITS-误差比例                float32\n",
+      "TSMixerx-NHITS-误差比例           float32\n",
+      "BiTCN-NHITS-误差比例              float32\n",
+      "TCN-NHITS-误差比例                float32\n",
+      "iTransformer-NHITS-误差比例       float32\n",
+      "StemGNN-NHITS-误差比例            float32\n",
+      "MLPMultivariate-NHITS-误差比例    float32\n",
+      "columns                        object\n",
+      "dtype: object\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
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+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'StemGNN', 'MLPMultivariate']\n",
+      "<class 'list'>\n",
+      "            ds            y        NHITS  upper_bound  lower_bound\n",
+      "0   2024-01-30  7315.000000  7314.250977  7255.317383  7406.355469\n",
+      "5   2024-01-31  7302.500000  7313.850586  7260.271484  7409.821777\n",
+      "10  2024-02-01  7275.000000  7296.995117  7250.980469  7385.459473\n",
+      "15  2024-02-02  7242.500000  7275.514648  7233.059082  7349.825684\n",
+      "20  2024-02-04  7240.000000  7236.788574  7210.264160  7308.604004\n",
+      "..         ...          ...          ...          ...          ...\n",
+      "975 2024-11-13  7323.333496  7332.291016  7241.626953  7422.700195\n",
+      "976 2024-11-14  7353.333496  7344.439941  7244.206543  7417.808594\n",
+      "977 2024-11-15  7393.333496  7319.736328  7230.743652  7418.842285\n",
+      "978 2024-11-18  7363.333496  7317.362305  7208.802734  7411.817871\n",
+      "979 2024-11-19  7370.000000  7307.738770  7272.761230  7399.986328\n",
+      "\n",
+      "[200 rows x 5 columns]\n"
+     ]
+    },
+    {
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",
+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_19344\\741737176.py:303: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00')\n",
+      "更新数据sqlUPDATE trueandpredict SET ds = '2024-11-20 00:00:00', NHITS = '7337.594', Informer = '7316.5264', LSTM = '7313.051', iTransformer = '7342.468', TSMixer = '7344.392', TSMixerx = '7306.8916', PatchTST = '7350.6377', RNN = '7378.303', GRU = '7405.318', TCN = '7418.5396', BiTCN = '7321.0723', DilatedRNN = '7320.571', MLP = '7338.035', DLinear = '7387.8057', NLinear = '7361.405', TFT = '7362.294', StemGNN = '7351.2534', MLPMultivariate = '7331.5254', TiDE = '7354.707', DeepNPTS = '7376.7446' WHERE ds = '2024-11-20 00:00:00'\n",
+      "更新数据sqlUPDATE trueandpredict SET ds = '2024-11-20 00:00:00', NHITS = '7337.594', Informer = '7316.5264', LSTM = '7313.051', iTransformer = '7342.468', TSMixer = '7344.392', TSMixerx = '7306.8916', PatchTST = '7350.6377', RNN = '7378.303', GRU = '7405.318', TCN = '7418.5396', BiTCN = '7321.0723', DilatedRNN = '7320.571', MLP = '7338.035', DLinear = '7387.8057', NLinear = '7361.405', TFT = '7362.294', StemGNN = '7351.2534', MLPMultivariate = '7331.5254', TiDE = '7354.707', DeepNPTS = '7376.7446' WHERE ds = '2024-11-20 00:00:00'\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Column 'ds' already exists in table 'trueandpredict'.\n",
+      "Column 'NHITS' already exists in table 'trueandpredict'.\n",
+      "Column 'Informer' already exists in table 'trueandpredict'.\n",
+      "Column 'LSTM' already exists in table 'trueandpredict'.\n",
+      "Column 'iTransformer' already exists in table 'trueandpredict'.\n",
+      "Column 'TSMixer' already exists in table 'trueandpredict'.\n",
+      "Column 'TSMixerx' already exists in table 'trueandpredict'.\n",
+      "Column 'PatchTST' already exists in table 'trueandpredict'.\n",
+      "Column 'RNN' already exists in table 'trueandpredict'.\n",
+      "Column 'GRU' already exists in table 'trueandpredict'.\n",
+      "Column 'TCN' already exists in table 'trueandpredict'.\n",
+      "Column 'BiTCN' already exists in table 'trueandpredict'.\n",
+      "Column 'DilatedRNN' already exists in table 'trueandpredict'.\n",
+      "Column 'MLP' already exists in table 'trueandpredict'.\n",
+      "Column 'DLinear' already exists in table 'trueandpredict'.\n",
+      "Column 'NLinear' already exists in table 'trueandpredict'.\n",
+      "Column 'TFT' already exists in table 'trueandpredict'.\n",
+      "Column 'StemGNN' already exists in table 'trueandpredict'.\n",
+      "Column 'MLPMultivariate' already exists in table 'trueandpredict'.\n",
+      "Column 'TiDE' already exists in table 'trueandpredict'.\n",
+      "Column 'DeepNPTS' already exists in table 'trueandpredict'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "训练数据绘图end\n",
+      "训练数据绘图end\n"
+     ]
+    }
+   ],
    "source": [
     "logger.info('模型训练完成')\n",
     "# # 模型评估\n",
@@ -306,206 +2423,71 @@
     "    with open(os.path.join(dataset,\"best_modelnames.txt\"), 'w') as f:\n",
     "        f.write(','.join(modelnames) + '\\n')\n",
     "\n",
-    "    # 预测值与真实值对比图\n",
-    "    plt.rcParams['font.sans-serif'] = ['SimHei']\n",
-    "    plt.figure(figsize=(15, 10))\n",
-    "    for n,model in enumerate(modelnames[:5]):\n",
-    "        plt.subplot(3, 2, n+1)\n",
-    "        plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值')\n",
-    "        plt.plot(df_combined3['ds'], df_combined3[model], label=model)\n",
-    "        plt.legend()\n",
-    "        plt.xlabel('日期')\n",
-    "        plt.ylabel('价格')\n",
-    "        plt.title(model+'拟合')\n",
-    "    plt.subplots_adjust(hspace=0.5)\n",
-    "    plt.savefig(os.path.join(dataset,'预测值与真实值对比图.png'), bbox_inches='tight')\n",
-    "    plt.close()\n",
+    "    def plot_predict_vs_true():\n",
+    "        # 预测值与真实值对比图\n",
+    "        plt.rcParams['font.sans-serif'] = ['SimHei']\n",
+    "        plt.figure(figsize=(15, 10))\n",
+    "        for n,model in enumerate(modelnames[:5]):\n",
+    "            plt.subplot(3, 2, n+1)\n",
+    "            plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值')\n",
+    "            plt.plot(df_combined3['ds'], df_combined3[model], label=model)\n",
+    "            plt.legend()\n",
+    "            plt.xlabel('日期')\n",
+    "            plt.ylabel('价格')\n",
+    "            plt.title(model+'拟合')\n",
+    "        plt.subplots_adjust(hspace=0.5)\n",
+    "        plt.savefig(os.path.join(dataset,'预测值与真实值对比图.png'), bbox_inches='tight')\n",
+    "        plt.close()\n",
     "    \n",
-    "\n",
+    "    plot_predict_vs_true()\n",
+    "    \n",
+    "    \n",
+    "    '''\n",
     "    # # 根据最佳模型的绝对误差的平均比例，最佳模型乘平均比例的百分比，确定最大最小值\n",
-    "    import heapq          # 使用堆来找到最大和最小的值\n",
-    "    def find_min_max_within_quantile(row):\n",
-    "        true_value = row['y']\n",
-    "        row.drop(['ds','y'], inplace=True)\n",
-    "        row = row.astype(float).round(2)\n",
-    "\n",
-    "        max_heap = []\n",
-    "        min_heap = []\n",
-    "        for col in row.index:\n",
-    "            # 对比真实值进行分类\n",
-    "            if row[col] < true_value:\n",
-    "                heapq.heappush(min_heap, row[col])\n",
-    "            elif row[col] > true_value:\n",
-    "                heapq.heappush(max_heap, -row[col])  # 使用负号来实现最大堆\n",
-    "\n",
-    "        if len(max_heap) == 1:\n",
-    "            max_y = max_heap[0]\n",
-    "        elif len(max_heap) == 0:\n",
-    "            max_y = -min_heap[-1]\n",
-    "        else:\n",
-    "            max_y = heapq.nsmallest(2, max_heap)[1]\n",
-    "\n",
-    "        if len(min_heap) < 2 :\n",
-    "            min_y = -max_heap[-1]\n",
-    "        else:\n",
-    "            min_y = heapq.nsmallest(2, min_heap)[-1]\n",
-    "\n",
-    "\n",
-    "        # 获取最大和最小的值\n",
-    "        q10 = min_y \n",
-    "        q90 = -max_y\n",
-    "\n",
-    "        # 获取最大和最小的模型名称\n",
-    "        min_model = row[row == q10].idxmin()\n",
-    "        max_model = row[row == q90].idxmax()\n",
-    "\n",
-    "        # 设置上下界比例\n",
-    "        rote = 1\n",
-    "\n",
-    "        q10 = q10 * rote\n",
-    "        q90 = q90 * rote\n",
-    "\n",
-    "        logger.info(min_model,q10,max_model,q90)\n",
-    "\n",
-    "        return pd.Series([q10, q90, min_model, max_model], index=['min_within_quantile', 'max_within_quantile', 'min_model', 'max_model'])\n",
-    "    # # 遍历行\n",
-    "    df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)\n",
-    "    df_combined = df_combined.round(4)\n",
-    "    print(df_combined3)\n",
-    "\n",
-    "\n",
-    "    '''\n",
-    "    # # 根据真实值y确定最大最小值,去掉最高最低的预测值\n",
-    "    import heapq          # 使用堆来找到最大和最小的值\n",
-    "    def find_min_max_within_quantile(row):\n",
-    "        true_value = row['y']\n",
-    "        row.drop(['ds','y'], inplace=True)\n",
-    "        row = row.astype(float).round(2)\n",
-    "\n",
-    "        max_heap = []\n",
-    "        min_heap = []\n",
-    "        for col in row.index:\n",
-    "            # 对比真实值进行分类\n",
-    "            if row[col] < true_value:\n",
-    "                heapq.heappush(min_heap, row[col])\n",
-    "            elif row[col] > true_value:\n",
-    "                heapq.heappush(max_heap, -row[col])  # 使用负号来实现最大堆\n",
-    "\n",
-    "        if len(max_heap) == 1:\n",
-    "            max_y = max_heap[0]\n",
-    "        elif len(max_heap) == 0:\n",
-    "            max_y = -min_heap[-1]\n",
-    "        else:\n",
-    "            max_y = heapq.nsmallest(2, max_heap)[1]\n",
-    "\n",
-    "        if len(min_heap) < 2 :\n",
-    "            min_y = -max_heap[-1]\n",
-    "        else:\n",
-    "            min_y = heapq.nsmallest(2, min_heap)[-1]\n",
-    "\n",
-    "\n",
-    "        # 获取最大和最小的值\n",
-    "        q10 = min_y \n",
-    "        q90 = -max_y\n",
-    "\n",
-    "        # 获取最大和最小的模型名称\n",
-    "        min_model = row[row == q10].idxmin()\n",
-    "        max_model = row[row == q90].idxmax()\n",
-    "\n",
-    "        # 设置上下界比例\n",
-    "        rote = 1\n",
-    "\n",
-    "        q10 = q10 * rote\n",
-    "        q90 = q90 * rote\n",
-    "\n",
-    "        logger.info(min_model,q10,max_model,q90)\n",
-    "\n",
-    "        return pd.Series([q10, q90, min_model, max_model], index=['min_within_quantile', 'max_within_quantile', 'min_model', 'max_model'])\n",
-    "    # # 遍历行\n",
-    "    df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)\n",
-    "    df_combined = df_combined.round(4)\n",
-    "    print(df_combined3)\n",
-    "    '''\n",
-    "\n",
-    "    '''\n",
-    "    #使用最佳五个模型进行绘图\n",
-    "    best_models = pd.read_csv(os.path.join(dataset,'best_modelnames.txt'),header=None).values.flatten().tolist()\n",
-    "    def find_min_max_within_quantile(row):\n",
-    "        row = row[best_models]\n",
-    "        q10 = row.min()\n",
-    "        q90 = row.max()\n",
-    "        # 获取 row行最大最小值模型名称\n",
-    "        min_model = row[row == q10].idxmin()\n",
-    "        max_model = row[row == q90].idxmin()\n",
-    "        \n",
-    "        # # 判断flot值是否为空值\n",
-    "        # if pd.isna(q10) or pd.isna(q90):\n",
-    "        return pd.Series([q10, q90,min_model,max_model], index=['min_within_quantile','max_within_quantile','min_model','max_model'])\n",
-    "\n",
-    "    # 遍历行\n",
-    "    df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)\n",
-    "    df_combined = df_combined.round(4)\n",
-    "    print(df_combined3)\n",
-    "    '''\n",
+    "    # 计算最佳模型对应的绝对误差的平均比例\n",
+    "    most_model_mae = model_results3[model_results3['模型(Model)']==most_model_name]['平均绝对误差(MAE)'].values[0]\n",
     "    \n",
-    "    '''\n",
-    "    # 通道使用模型评估前80%作为置信度\n",
-    "    def find_min_max_within_quantile(row):\n",
-    "        row.drop(['ds','y'], inplace=True)\n",
-    "        row = row.astype(float).round(2)\n",
-    "\n",
-    "        row_sorted = row\n",
-    "        # 计算 10% 和 90% 位置的索引\n",
-    "        index_10 = 0\n",
-    "        index_90 = int(len(row_sorted) * 0.8)\n",
-    "        q10 = row_sorted[index_10]\n",
-    "        q90 = row_sorted[index_90]\n",
-    "        # 获取模型名称\n",
-    "        min_model = row[row == q10].idxmin()\n",
-    "        max_model = row[row == q90].idxmin()\n",
-    "\n",
-    "        \n",
-    "        # # 判断flot值是否为空值\n",
-    "        # if pd.isna(q10) or pd.isna(q90):\n",
-    "        return pd.Series([q10, q90,min_model,max_model], index=['min_within_quantile','max_within_quantile','min_model','max_model'])\n",
-    "\n",
-    "    # 重新排列\n",
-    "    df_combined3 = df_combined3[['ds','y'] + allmodelnames]\n",
-    "    # 遍历行\n",
-    "    df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)\n",
-    "    df_combined = df_combined.round(4)\n",
-    "    print(df_combined3)\n",
-    "\n",
-    "    '''\n",
+    "    def mae_upper_lower(row):\n",
+    "        # 计算上边界值\n",
+    "        upper_bound = row[most_model_name] * (1 + most_model_mae/row[most_model_name])\n",
+    "        # 计算下边界值\n",
+    "        lower_bound = row[most_model_name] * (1 - most_model_mae/row[most_model_name])\n",
+    "        return pd.Series([lower_bound, upper_bound], index=['lower_bound', 'upper_bound'])\n",
     "    \n",
-    "    '''\n",
-    "    # 通道使用预测模型的80%置信度\n",
-    "    def find_min_max_within_quantile(row):\n",
-    "        row.drop(['ds','y'], inplace=True)\n",
-    "        row = row.astype(float).round(2)\n",
-    "\n",
-    "        row_sorted = row.sort_values(ascending=True).reset_index(drop=True)\n",
-    "        # 计算 10% 和 90% 位置的索引\n",
-    "        index_10 = int(len(row_sorted) * 0.1)\n",
-    "        index_90 = int(len(row_sorted) * 0.9)\n",
-    "        q10 = row_sorted[index_10]\n",
-    "        q90 = row_sorted[index_90]\n",
-    "        # 获取模型名称\n",
-    "        min_model = row[row == q10].idxmin()\n",
-    "        max_model = row[row == q90].idxmin()\n",
-    "\n",
-    "        \n",
-    "        # # 判断flot值是否为空值\n",
-    "        # if pd.isna(q10) or pd.isna(q90):\n",
-    "        return pd.Series([q10, q90,min_model,max_model], index=['min_within_quantile','max_within_quantile','min_model','max_model'])\n",
-    "\n",
-    "    # 遍历行\n",
-    "    df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)\n",
-    "    df_combined = df_combined.round(4)\n",
-    "    print(df_combined3)\n",
+    "    df_combined3[['upper_bound', 'lower_bound']] = df_combined3.apply(mae_upper_lower, axis=1)\n",
     "    '''\n",
     "    \n",
     "\n",
+    "    # 计算每个模型与最佳模型的绝对误差比例\n",
+    "    names = []\n",
+    "    for col in allmodelnames:\n",
+    "        df_combined3[f'{col}-{most_model_name}-误差比例'] = abs(df_combined3[col] - df_combined3[most_model_name]) / df_combined3[most_model_name] * 100\n",
+    "        names.append(f'{col}-{most_model_name}-误差比例')\n",
+    "\n",
+    "    # 设置阈值 rote\n",
+    "    rote = 1.5\n",
+    "    names_df = df_combined3[names]\n",
+    "    # names_df['rote'] = rote\n",
+    "    def add_rote_column(row):\n",
+    "        columns = []\n",
+    "        for r in names_df.columns:\n",
+    "            if row[r] <= rote:\n",
+    "                columns.append(r.split('-')[0])\n",
+    "        return pd.Series([columns], index=['columns'])\n",
+    "    names_df['columns'] = names_df.apply(add_rote_column, axis=1)\n",
+    "    \n",
+    "    def add_upper_lower_bound(row):\n",
+    "        print(row['columns'])\n",
+    "        print(type(row['columns']))\n",
+    "        # 计算上边界值\n",
+    "        upper_bound = df_combined3.loc[row.name,row['columns']].max()\n",
+    "        # 计算下边界值\n",
+    "        lower_bound = df_combined3.loc[row.name,row['columns']].min()\n",
+    "        return pd.Series([lower_bound, upper_bound], index=['lower_bound', 'upper_bound'])\n",
+    "    df_combined3[['upper_bound','lower_bound']] = names_df.apply(add_upper_lower_bound, axis=1)\n",
+    "    \n",
+    "    print(df_combined3[['ds','y',most_model_name,'upper_bound','lower_bound']])\n",
+    "    \n",
     "    '''\n",
     "    # 计算波动率,取近60日波动率的10%和90%分位数确定通道上下界\n",
     "    df_combined3['volatility'] = df_combined3['y'].pct_change().round(4)\n",
@@ -557,9 +2539,39 @@
     "    # 去除有空值的行\n",
     "    df_combined3.dropna(inplace=True)\n",
     "    # 保存到数据库\n",
-    "    df_combined3.to_sql('testandpredict_groupby', sqlitedb.connection, if_exists='replace', index=False)\n",
+    "    # df_combined3.to_sql('testandpredict_groupby', sqlitedb.connection, if_exists='replace', index=False)\n",
     "    df_combined3.to_csv(os.path.join(dataset,\"testandpredict_groupby.csv\"),index=False)\n",
     "\n",
+    "    \n",
+    "    def _plt_predict_ture(df):\n",
+    "        df = df[-50:] # 取50个数据点画图\n",
+    "        # 历史价格\n",
+    "        plt.figure(figsize=(20, 10))\n",
+    "        plt.plot(df['ds'], df['y'], label='真实值')\n",
+    "        # 颜色填充\n",
+    "        # plt.fill_between(df['ds'], df['min_within_quantile'], df['max_within_quantile'], alpha=0.2)\n",
+    "        plt.fill_between(df['ds'], df['upper_bound'], df['lower_bound'], alpha=0.2)\n",
+    "        # plt.plot(df_combined3['ds'], df_combined3['min_abs_error_rate_prediction'], label='最小绝对误差', linestyle='--', color='orange')\n",
+    "        # 网格\n",
+    "        plt.grid(True)\n",
+    "        # 显示历史值\n",
+    "        for i, j in zip(df['ds'], df['y']):\n",
+    "            plt.text(i, j, str(j), ha='center', va='bottom')\n",
+    "\n",
+    "        for model in most_model:\n",
+    "            plt.plot(df['ds'], df[model], label=model,marker='o')\n",
+    "        # 当前日期画竖虚线\n",
+    "        plt.axvline(x=df['ds'].iloc[-horizon], color='r', linestyle='--')\n",
+    "        plt.legend()\n",
+    "        plt.xlabel('日期')\n",
+    "        plt.ylabel('价格')\n",
+    "        \n",
+    "        plt.savefig(os.path.join(dataset,'历史价格-预测值.png'), bbox_inches='tight')\n",
+    "        plt.show()\n",
+    "        plt.close()\n",
+    "\n",
+    "    _plt_predict_ture(df_combined3)\n",
+    "\n",
     "    '''\n",
     "   # 去掉方差最大的模型，其余模型预测最大最小值确定通道边界\n",
     "    \n",
@@ -737,30 +2749,6 @@
     "    #         sqlitedb.insert_data('testandpredict_groupby',tuple(row_dict.values()),columns=row_dict.keys())\n",
     "    \n",
     "    \n",
-    "    def _plt_predict_ture(df):\n",
-    "        df = df[-50:] # 取50个数据点画图\n",
-    "        # 历史价格\n",
-    "        plt.figure(figsize=(20, 10))\n",
-    "        plt.plot(df['ds'], df['y'], label='真实值')\n",
-    "        # 颜色填充\n",
-    "        plt.fill_between(df['ds'], df['min_within_quantile'], df['max_within_quantile'], alpha=0.2)\n",
-    "        # plt.plot(df_combined3['ds'], df_combined3['min_abs_error_rate_prediction'], label='最小绝对误差', linestyle='--', color='orange')\n",
-    "        # 网格\n",
-    "        plt.grid(True)\n",
-    "        # 显示历史值\n",
-    "        for i, j in zip(df['ds'], df['y']):\n",
-    "            plt.text(i, j, str(j), ha='center', va='bottom')\n",
-    "\n",
-    "        for model in most_model:\n",
-    "            plt.plot(df['ds'], df[model], label=model,marker='o')\n",
-    "        # 当前日期画竖虚线\n",
-    "        plt.axvline(x=df['ds'].iloc[-horizon], color='r', linestyle='--')\n",
-    "        plt.legend()\n",
-    "        plt.xlabel('日期')\n",
-    "        plt.ylabel('价格')\n",
-    "        \n",
-    "        plt.savefig(os.path.join(dataset,'历史价格-预测值.png'), bbox_inches='tight')\n",
-    "        plt.close()\n",
     "\n",
     "    def _plt_predict_table(df): \n",
     "        # 预测值表格\n",
@@ -795,7 +2783,7 @@
     "        plt.savefig(os.path.join(dataset,'模型评估.png'), bbox_inches='tight')\n",
     "        plt.close()\n",
     "\n",
-    "    _plt_predict_ture(df_combined3)\n",
+    "\n",
     "    _plt_predict_table(df_combined3)\n",
     "    _plt_model_results3()\n",
     "\n",