聚烯烃绘图逻辑，舍弃方差最大的模型

2024-11-20 10:44:21 +08:00 · 2024-11-20 10:44:21 +08:00 · c670c7a7eb
commit c670c7a7eb
parent dbe9c17a58
95 changed files with 14974 additions and 37828 deletions
--- a/config_juxiting.py
+++ b/config_juxiting.py
@ -220,7 +220,7 @@ is_update_eta  = False  # 预测结果上传到eta
 is_update_report = False # 是否上传报告
 # 数据截取日期
-end_time = '' # 数据截取日期
+end_time = '2024-11-01' # 数据截取日期
 delweekenday = True
 is_corr = False # 特征是否参与滞后领先提升相关系数
 add_kdj = False # 是否添加kdj指标
--- a/订单-成品天数与价格散点图.png
+++ b/订单-成品天数与价格散点图.png
--- a/juxitingdataset/BOPP利润（华东地区）与价格散点图.png
+++ b/juxitingdataset/BOPP利润（华东地区）与价格散点图.png
--- a/juxitingdataset/BOPP订单天数-产成品库存天数（隆众）与价格散点图.png
+++ b/juxitingdataset/BOPP订单天数-产成品库存天数（隆众）与价格散点图.png
--- a/juxitingdataset/BOPP：成本：中国（日）与价格散点图.png
+++ b/juxitingdataset/BOPP：成本：中国（日）与价格散点图.png
--- a/juxitingdataset/Brent-WTI与价格散点图.png
+++ b/juxitingdataset/Brent-WTI与价格散点图.png
--- a/juxitingdataset/FEI丙烷
+++ b/juxitingdataset/FEI丙烷
--- a/juxitingdataset/L-P价差与价格散点图.png
+++ b/juxitingdataset/L-P价差与价格散点图.png
--- a/juxitingdataset/L-P价差（盘面）与价格散点图.png
+++ b/juxitingdataset/L-P价差（盘面）与价格散点图.png
--- a/juxitingdataset/PDH制PP利润（山东）与价格散点图.png
+++ b/juxitingdataset/PDH制PP利润（山东）与价格散点图.png
--- a/M4(含税成本)与价格散点图.png
+++ b/M4(含税成本)与价格散点图.png
--- a/juxitingdataset/PP-SC价差（7吨桶比）与价格散点图.png
+++ b/juxitingdataset/PP-SC价差（7吨桶比）与价格散点图.png
--- a/juxitingdataset/PP下游原料库存与价格散点图.png
+++ b/juxitingdataset/PP下游原料库存与价格散点图.png
--- a/juxitingdataset/PP下游原料库存天数与价格散点图.png
+++ b/juxitingdataset/PP下游原料库存天数与价格散点图.png
--- a/juxitingdataset/PP下游成品库存天数与价格散点图.png
+++ b/juxitingdataset/PP下游成品库存天数与价格散点图.png
--- a/juxitingdataset/PP周度产量与价格散点图.png
+++ b/juxitingdataset/PP周度产量与价格散点图.png
--- a/juxitingdataset/PP周度开工率与价格散点图.png
+++ b/juxitingdataset/PP周度开工率与价格散点图.png
--- a/juxitingdataset/PP库存(PDH)与价格散点图.png
+++ b/juxitingdataset/PP库存(PDH)与价格散点图.png
--- a/juxitingdataset/PP开工率-2WMA与价格散点图.png
+++ b/juxitingdataset/PP开工率-2WMA与价格散点图.png
--- a/juxitingdataset/PP开工率-7DMA与价格散点图.png
+++ b/juxitingdataset/PP开工率-7DMA与价格散点图.png
--- a/juxitingdataset/PP开工率（1MMA）与价格散点图.png
+++ b/juxitingdataset/PP开工率（1MMA）与价格散点图.png
--- a/juxitingdataset/PP指标数据.xlsx
+++ b/juxitingdataset/PP指标数据.xlsx
--- a/juxitingdataset/PP无纺布：厂内库存：中国（周）与价格散点图.png
+++ b/juxitingdataset/PP无纺布：厂内库存：中国（周）与价格散点图.png
--- a/juxitingdataset/PP日度产量7DMA与价格散点图.png
+++ b/juxitingdataset/PP日度产量7DMA与价格散点图.png
--- a/juxitingdataset/PP日度产量与价格散点图.png
+++ b/juxitingdataset/PP日度产量与价格散点图.png
--- a/juxitingdataset/PP日度产量（粒料+粉料）与价格散点图.png
+++ b/juxitingdataset/PP日度产量（粒料+粉料）与价格散点图.png
--- a/juxitingdataset/PP日度开工率与价格散点图.png
+++ b/juxitingdataset/PP日度开工率与价格散点图.png
--- a/juxitingdataset/PP期末库存-天津港（周）与价格散点图.png
+++ b/juxitingdataset/PP期末库存-天津港（周）与价格散点图.png
--- a/juxitingdataset/PP期货-丙烯价差（山东）-400DMA与价格散点图.png
+++ b/juxitingdataset/PP期货-丙烯价差（山东）-400DMA与价格散点图.png
--- a/juxitingdataset/PP神华竞拍（临沂）与价格散点图.png
+++ b/juxitingdataset/PP神华竞拍（临沂）与价格散点图.png
--- a/juxitingdataset/PP神华竞拍（华东）与价格散点图.png
+++ b/juxitingdataset/PP神华竞拍（华东）与价格散点图.png
--- a/juxitingdataset/PP粉检修减损量（周）与价格散点图.png
+++ b/juxitingdataset/PP粉检修减损量（周）与价格散点图.png
--- a/juxitingdataset/PP贸易商库存(钢联)同比与价格散点图.png
+++ b/juxitingdataset/PP贸易商库存(钢联)同比与价格散点图.png
--- a/juxitingdataset/PP连续-1月与价格散点图.png
+++ b/juxitingdataset/PP连续-1月与价格散点图.png
--- a/juxitingdataset/PP连续-5月与价格散点图.png
+++ b/juxitingdataset/PP连续-5月与价格散点图.png
--- a/juxitingdataset/PP连续-9月与价格散点图.png
+++ b/juxitingdataset/PP连续-9月与价格散点图.png
--- a/juxitingdataset/PP部分下游订单天数与价格散点图.png
+++ b/juxitingdataset/PP部分下游订单天数与价格散点图.png
--- a/juxitingdataset/PP：BOPP：1103K：出厂价：华北地区：国家能源宁煤（日）与价格散点图.png
+++ b/juxitingdataset/PP：BOPP：1103K：出厂价：华北地区：国家能源宁煤（日）与价格散点图.png
--- a/juxitingdataset/PP：拉丝：S1003：市场主流价：常州：国能榆林化工（日）与价格散点图.png
+++ b/juxitingdataset/PP：拉丝：S1003：市场主流价：常州：国能榆林化工（日）与价格散点图.png
--- a/juxitingdataset/best_modelnames.txt
+++ b/juxitingdataset/best_modelnames.txt
@ -1 +1 @@
-NLinear,RNN,TiDE,LSTM,DilatedRNN
+NLinear,RNN,TiDE,TSMixer,PatchTST
--- a/juxitingdataset/cross_validation.csv
+++ b/juxitingdataset/cross_validation.csv
--- 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
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-2024-11-12,7341.77,7294.74,7349.73,7357.31,7360.36,7414.32,7341.37,7347.22,7365.44,7470.71,7359.69,7316.59,7389.09,7367.12,7335.55,7337.95,7350.08,7408.84,7358.53,7392.38,7306.67
+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
-2024-11-13,7381.98,7287.19,7351.89,7350.5,7376.17,7422.79,7383.6,7340.75,7387.01,7469.26,7353.83,7317.99,7432.26,7361.68,7364.91,7332.75,7348.14,7431.79,7365.84,7405.65,7323.33
+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
-2024-11-14,7382.61,7254.22,7354.04,7348.43,7380.54,7393.63,7401.79,7312.58,7382.11,7465.35,7365.01,7333.27,7446.75,7349.96,7346.71,7327.26,7336.3,7436.18,7378.39,7422.76,7353.33
+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
-2024-11-15,7381.57,7295.52,7368.15,7328.46,7378.27,7388.9,7390.94,7306.41,7388.54,7474.63,7368.29,7331.23,7442.02,7386.59,7333.92,7323.29,7347.5,7405.92,7384.14,7402.9,7393.33
+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
-2024-11-18,7351.67,7319.07,7406.21,7316.12,7375.26,7372.63,7385.84,7357.07,7391.6,7479.03,7371.18,7359.46,7430.76,7360.74,7375.31,7320.7,7358.16,7380.2,7378.35,7394.49,7363.33
+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
-2024-11-19,7359.2,7325.43,7374.63,7342.03,7352.78,7343.61,7342.73,7372.25,7381.78,7449.07,7317.24,7350.61,7326.66,7375.86,7335.6,7364.06,7345.6,7354.37,7365.84,7367.14,
+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,
-2024-11-20,7339.67,7303.26,7375.4,7320.67,7341.12,7339.04,7337.23,7369.62,7401.5,7449.81,7307.54,7353.7,7321.71,7326.19,7327.7,7355.31,7343.62,7344.18,7327.17,7372.86,
+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,
-2024-11-21,7313.64,7310.42,7380.75,7315.62,7321.95,7387.1,7330.4,7346.73,7399.87,7443.93,7302.45,7366.74,7310.2,7341.94,7341.78,7348.73,7331.54,7333.94,7305.5,7363.12,
+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,
-2024-11-22,7296.48,7321.02,7392.48,7309.1,7300.34,7376.25,7299.74,7331.89,7405.18,7451.16,7291.02,7360.91,7294.78,7323.82,7406.11,7343.82,7342.97,7315.08,7304.24,7363.37,
+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,
-2024-11-25,7286.7,7319.39,7421.41,7314.21,7278.51,7335.19,7267.8,7370.68,7405.27,7454.45,7288.73,7384.35,7295.84,7330.96,7369.37,7341.7,7353.84,7304.88,7295.69,7353.22,
+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,
--- a/juxitingdataset/jbsh_juxiting.db
+++ b/juxitingdataset/jbsh_juxiting.db
--- a/juxitingdataset/model_evaluation.csv
+++ b/juxitingdataset/model_evaluation.csv
@ -1,21 +1,21 @@
 模型(Model),平均平方误差(MSE),均方根误差(RMSE),平均绝对误差(MAE)
-NLinear,811.576,28.488,19.74
+NLinear,845.356,29.075,20.267
-RNN,897.124,29.952,22.074
+RNN,972.286,31.182,22.988
-TiDE,1015.113,31.861,23.22
+TiDE,1028.451,32.069,23.463
-LSTM,1072.179,32.744,25.674
+TSMixer,1302.964,36.097,25.947
-DilatedRNN,1323.952,36.386,28.347
+PatchTST,1328.982,36.455,25.99
-PatchTST,1353.449,36.789,26.175
+NHITS,1562.545,39.529,29.168
-TSMixer,1467.982,38.314,28.163
+DilatedRNN,1841.868,42.917,31.132
-NHITS,1589.956,39.874,30.203
+DLinear,1865.095,43.187,30.754
-GRU,1764.144,42.002,35.821
+GRU,1948.117,44.137,37.899
-DLinear,1831.303,42.794,30.357
+LSTM,3160.178,56.215,46.181
-DeepNPTS,2890.076,53.759,38.553
+Informer,3291.08,57.368,42.942
-TFT,3040.148,55.138,42.36
+DeepNPTS,3342.861,57.817,42.448
-MLP,3459.517,58.818,45.679
+MLP,3377.778,58.119,44.678
-Informer,3635.914,60.299,44.59
+TFT,3480.96,59.0,45.242
-TSMixerx,4352.664,65.975,49.521
+TSMixerx,4424.076,66.514,50.562
-BiTCN,4825.292,69.464,53.842
+BiTCN,4426.452,66.532,51.306
-TCN,6703.221,81.873,65.098
+TCN,6855.79,82.8,68.446
-iTransformer,12283.86,110.833,78.95
+iTransformer,11338.897,106.484,86.463
-MLPMultivariate,12681.278,112.611,82.881
+StemGNN,12423.222,111.46,91.022
-StemGNN,12731.685,112.835,91.7
+MLPMultivariate,12947.138,113.785,84.447
--- a/juxitingdataset/predict.csv
+++ b/juxitingdataset/predict.csv
@ -1,6 +1,6 @@
 unique_id,ds,NHITS,Informer,LSTM,iTransformer,TSMixer,TSMixerx,PatchTST,RNN,GRU,TCN,BiTCN,DilatedRNN,MLP,DLinear,NLinear,TFT,FEDformer,StemGNN,MLPMultivariate,TiDE,DeepNPTS
-1,2024-11-19,7359.205,7325.434,7374.6274,7342.029,7352.7837,7343.613,7342.73,7372.2515,7381.781,7449.0703,7317.2397,7350.607,7326.661,7375.8584,7335.603,7364.06,,7345.604,7354.3696,7365.8433,7367.143
+1,2024-11-20,7337.594,7316.5264,7313.051,7342.468,7344.392,7306.8916,7350.6377,7378.303,7405.318,7418.5396,7321.0723,7320.571,7338.035,7387.8057,7361.405,7362.294,,7351.2534,7331.5254,7354.707,7376.7446
-1,2024-11-20,7339.673,7303.256,7375.402,7320.6665,7341.1157,7339.045,7337.2275,7369.62,7401.5024,7449.8086,7307.5415,7353.703,7321.7114,7326.1914,7327.6973,7355.3086,,7343.625,7344.175,7327.1714,7372.8594
+1,2024-11-21,7317.166,7307.525,7316.9746,7328.6523,7332.2124,7313.822,7338.1885,7370.3306,7411.06,7428.9434,7317.8125,7327.2505,7336.689,7338.9487,7351.548,7367.47,,7347.7666,7312.2354,7317.218,7363.681
-1,2024-11-21,7313.6357,7310.424,7380.7524,7315.6226,7321.9478,7387.099,7330.402,7346.7344,7399.866,7443.933,7302.4478,7366.744,7310.202,7341.9395,7341.784,7348.7305,,7331.544,7333.9355,7305.503,7363.116
+1,2024-11-22,7297.237,7303.1304,7326.406,7326.8555,7312.2275,7317.625,7323.693,7346.49,7403.6733,7445.0225,7312.6143,7330.3555,7338.735,7334.716,7364.716,7364.164,,7336.8613,7308.5083,7293.3335,7345.6753
-1,2024-11-22,7296.4775,7321.025,7392.479,7309.1006,7300.343,7376.246,7299.7417,7331.8857,7405.181,7451.1577,7291.023,7360.911,7294.7803,7323.8164,7406.1123,7343.8184,,7342.9673,7315.0786,7304.238,7363.372
+1,2024-11-25,7303.1514,7296.904,7344.721,7335.4663,7295.3184,7333.2104,7318.1465,7358.585,7420.508,7441.583,7302.0195,7356.277,7322.7534,7303.1694,7395.4727,7359.9365,,7359.6836,7302.15,7295.5586,7386.0483
-1,2024-11-25,7286.6978,7319.3916,7421.4077,7314.2144,7278.512,7335.186,7267.798,7370.676,7405.2725,7454.4453,7288.7314,7384.3545,7295.8447,7330.956,7369.3687,7341.7007,,7353.839,7304.878,7295.6865,7353.2183
+1,2024-11-26,7291.9507,7311.975,7373.8174,7342.075,7280.3906,7364.2593,7291.8506,7386.514,7420.5625,7449.439,7294.8267,7374.346,7319.2783,7310.3,7329.5864,7357.711,,7349.3394,7286.713,7291.1426,7341.7344
--- a/juxitingdataset/testandpredict_groupby.csv
+++ b/juxitingdataset/testandpredict_groupby.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
-2024-01-29,7327.54,7371.29,7327.98,7384.88,7303.02,7287.33,7292.89,7315.12,7343.6,7448.81,7326.19,7305.51,7323.52,7296.29,7310.33,7284.56,7305.08,7307.85,7296.64,7296.58,7305.0
+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
-2024-01-30,7323.87,7345.87,7337.31,7389.43,7298.24,7304.34,7293.09,7323.49,7353.22,7449.26,7332.46,7312.26,7328.59,7281.35,7304.5,7300.72,7302.7,7318.95,7297.0,7304.78,7315.0
+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
-2024-01-31,7324.15,7346.47,7344.21,7380.46,7308.31,7304.1,7294.49,7331.7,7359.12,7457.24,7342.73,7323.04,7320.56,7292.95,7297.57,7318.6,7301.14,7334.67,7303.24,7314.47,7302.5
+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
-2024-02-01,7310.36,7350.73,7330.01,7368.2,7301.07,7305.61,7286.25,7318.49,7346.67,7454.66,7335.99,7307.86,7320.18,7289.85,7286.74,7300.7,7300.0,7329.82,7302.62,7301.44,7275.0
+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
-2024-02-02,7293.69,7327.78,7309.33,7373.02,7273.72,7281.96,7266.09,7301.23,7324.77,7439.28,7308.17,7276.55,7291.01,7277.53,7261.4,7276.11,7297.48,7296.47,7281.35,7281.49,7242.5
+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
-2024-02-04,7254.43,7283.66,7289.7,7339.78,7239.73,7246.94,7231.94,7275.87,7300.41,7419.08,7273.83,7247.99,7247.88,7248.6,7243.9,7245.49,7293.53,7245.1,7250.1,7275.04,7240.0
+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
-2024-02-05,7250.29,7259.14,7291.3,7286.88,7225.65,7210.31,7228.4,7274.82,7302.66,7410.31,7243.09,7250.31,7235.75,7227.82,7230.87,7230.15,7289.29,7232.53,7231.23,7243.13,7227.5
+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
-2024-02-06,7238.12,7245.94,7284.38,7245.93,7214.97,7191.32,7208.56,7264.12,7290.83,7402.4,7214.51,7247.19,7215.73,7210.97,7213.27,7208.05,7284.47,7215.47,7216.57,7240.12,7246.67
+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
-2024-02-07,7250.07,7249.58,7298.53,7246.31,7225.56,7233.26,7233.51,7282.69,7310.77,7411.92,7197.42,7269.31,7226.51,7226.73,7229.02,7225.51,7280.56,7230.76,7231.61,7227.76,7255.0
+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
-2024-02-08,7255.89,7238.05,7302.06,7239.05,7240.02,7246.68,7249.72,7290.17,7314.03,7416.26,7187.06,7274.24,7236.22,7231.46,7251.08,7240.22,7277.06,7237.26,7253.37,7246.68,7270.0
+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
-2024-02-18,7255.75,7235.38,7313.8,7233.69,7255.69,7255.58,7265.83,7300.25,7328.57,7421.75,7188.82,7284.88,7247.14,7230.47,7268.08,7275.0,7274.52,7246.07,7273.11,7254.99,7336.67
+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
-2024-02-19,7328.24,7286.07,7381.02,7275.31,7308.41,7310.31,7327.43,7366.18,7388.41,7447.08,7280.03,7355.52,7284.43,7281.5,7344.23,7354.4,7273.63,7291.66,7323.83,7270.38,7350.0
+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
-2024-02-20,7340.94,7314.03,7381.57,7320.35,7338.18,7366.51,7341.79,7363.28,7388.78,7459.81,7353.13,7385.1,7303.85,7305.54,7355.22,7374.68,7273.2,7317.23,7334.72,7315.85,7335.0
+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
-2024-02-21,7328.09,7291.25,7364.24,7319.25,7332.97,7412.64,7331.43,7349.38,7375.82,7465.63,7385.73,7387.23,7307.38,7333.67,7337.79,7342.36,7272.39,7318.1,7325.39,7314.47,7297.5
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-2024-10-31,7312.56,7305.57,7340.64,7456.83,7354.07,7421.74,7296.75,7337.7,7358.05,7471.7,7350.27,7369.02,7348.39,7402.12,7340.37,7326.14,7372.69,7468.82,7330.87,7388.27,7340.0
+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
-2024-11-01,7343.08,7325.18,7365.97,7418.3,7350.28,7392.95,7324.07,7355.09,7382.28,7474.65,7365.08,7377.86,7372.56,7387.91,7340.9,7339.71,7367.81,7459.85,7335.55,7377.83,7363.33
+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
-2024-11-04,7386.98,7346.15,7387.89,7369.52,7364.33,7391.78,7341.57,7376.17,7396.97,7479.09,7375.33,7398.98,7399.13,7360.83,7360.96,7393.32,7364.03,7461.72,7357.11,7379.33,7360.0
+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
-2024-11-05,7391.64,7351.35,7380.57,7346.31,7361.38,7392.48,7346.13,7362.84,7391.88,7478.33,7376.7,7386.92,7404.12,7376.09,7372.73,7392.84,7360.4,7451.46,7349.65,7347.05,7357.5
+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
-2024-11-06,7377.09,7346.7,7380.05,7335.56,7358.76,7378.26,7347.79,7375.06,7391.63,7476.48,7358.81,7383.4,7388.88,7361.92,7379.12,7385.59,7356.97,7412.7,7342.42,7377.96,7383.33
+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
-2024-11-07,7388.92,7358.83,7413.9,7342.16,7375.17,7391.05,7372.72,7398.94,7420.15,7477.76,7361.6,7396.03,7409.27,7366.39,7401.27,7405.46,7355.01,7421.77,7363.14,7384.18,7366.67
+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
-2024-11-08,7353.58,7344.83,7383.68,7340.64,7369.75,7386.01,7355.9,7370.14,7393.42,7466.26,7359.23,7360.38,7396.27,7382.97,7375.96,7376.72,7353.13,7417.78,7356.34,7372.99,7383.33
+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
-2024-11-11,7379.68,7363.24,7413.31,7352.35,7379.62,7397.03,7377.89,7396.57,7420.56,7476.66,7373.14,7364.99,7411.73,7361.13,7384.96,7384.97,7352.16,7430.21,7378.04,7383.47,7340.0
+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
-2024-11-12,7341.77,7294.74,7349.73,7357.31,7360.36,7414.32,7341.37,7347.22,7365.44,7470.71,7359.69,7316.59,7389.09,7367.12,7335.55,7337.95,7350.08,7408.84,7358.53,7392.38,7306.67
+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
-2024-11-13,7381.98,7287.19,7351.89,7350.5,7376.17,7422.79,7383.6,7340.75,7387.01,7469.26,7353.83,7317.99,7432.26,7361.68,7364.91,7332.75,7348.14,7431.79,7365.84,7405.65,7323.33
+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
-2024-11-14,7382.61,7254.22,7354.04,7348.43,7380.54,7393.63,7401.79,7312.58,7382.11,7465.35,7365.01,7333.27,7446.75,7349.96,7346.71,7327.26,7336.3,7436.18,7378.39,7422.76,7353.33
+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
-2024-11-15,7381.57,7295.52,7368.15,7328.46,7378.27,7388.9,7390.94,7306.41,7388.54,7474.63,7368.29,7331.23,7442.02,7386.59,7333.92,7323.29,7347.5,7405.92,7384.14,7402.9,7393.33
+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
-2024-11-18,7351.67,7319.07,7406.21,7316.12,7375.26,7372.63,7385.84,7357.07,7391.6,7479.03,7371.18,7359.46,7430.76,7360.74,7375.31,7320.7,7358.16,7380.2,7378.35,7394.49,7363.33
+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
-2024-11-19,7359.2,7325.43,7374.63,7342.03,7352.78,7343.61,7342.73,7372.25,7381.78,7449.07,7317.24,7350.61,7326.66,7375.86,7335.6,7364.06,7345.6,7354.37,7365.84,7367.14,
+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,
-2024-11-20,7339.67,7303.26,7375.4,7320.67,7341.12,7339.04,7337.23,7369.62,7401.5,7449.81,7307.54,7353.7,7321.71,7326.19,7327.7,7355.31,7343.62,7344.18,7327.17,7372.86,
+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,
-2024-11-21,7313.64,7310.42,7380.75,7315.62,7321.95,7387.1,7330.4,7346.73,7399.87,7443.93,7302.45,7366.74,7310.2,7341.94,7341.78,7348.73,7331.54,7333.94,7305.5,7363.12,
+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,
-2024-11-22,7296.48,7321.02,7392.48,7309.1,7300.34,7376.25,7299.74,7331.89,7405.18,7451.16,7291.02,7360.91,7294.78,7323.82,7406.11,7343.82,7342.97,7315.08,7304.24,7363.37,
+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,
-2024-11-25,7286.7,7319.39,7421.41,7314.21,7278.51,7335.19,7267.8,7370.68,7405.27,7454.45,7288.73,7384.35,7295.84,7330.96,7369.37,7341.7,7353.84,7304.88,7295.69,7353.22,
+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,
--- a/juxitingdataset/中国BOPP周度产成品库存天数（T）与价格散点图.png
+++ b/juxitingdataset/中国BOPP周度产成品库存天数（T）与价格散点图.png
--- a/juxitingdataset/中国BOPP成品库存量与价格散点图.png
+++ b/juxitingdataset/中国BOPP成品库存量与价格散点图.png
--- a/juxitingdataset/中国BOPP订单天数-4WMA与价格散点图.png
+++ b/juxitingdataset/中国BOPP订单天数-4WMA与价格散点图.png
--- a/juxitingdataset/中国CPP成品库存天数与价格散点图.png
+++ b/juxitingdataset/中国CPP成品库存天数与价格散点图.png
--- a/juxitingdataset/中国CPP订单天数与价格散点图.png
+++ b/juxitingdataset/中国CPP订单天数与价格散点图.png
--- a/juxitingdataset/中国PP无纺布原料库存天数与价格散点图.png
+++ b/juxitingdataset/中国PP无纺布原料库存天数与价格散点图.png
--- a/juxitingdataset/中国PP无纺布原料库存量与价格散点图.png
+++ b/juxitingdataset/中国PP无纺布原料库存量与价格散点图.png
--- a/juxitingdataset/中国PP无纺布成品库存天数与价格散点图.png
+++ b/juxitingdataset/中国PP无纺布成品库存天数与价格散点图.png
--- a/juxitingdataset/中国再生PP样本企业周度库存与价格散点图.png
+++ b/juxitingdataset/中国再生PP样本企业周度库存与价格散点图.png
--- a/juxitingdataset/中国：华东地区：市场平均价：BOPP厚光膜与价格散点图.png
+++ b/juxitingdataset/中国：华东地区：市场平均价：BOPP厚光膜与价格散点图.png
--- a/juxitingdataset/价差类指标相关性直方分布图.png
+++ b/juxitingdataset/价差类指标相关性直方分布图.png
--- a/juxitingdataset/价格类指标相关性直方分布图.png
+++ b/juxitingdataset/价格类指标相关性直方分布图.png
--- a/juxitingdataset/低熔共聚-拉丝价差（华南）与价格散点图.png
+++ b/juxitingdataset/低熔共聚-拉丝价差（华南）与价格散点图.png
--- a/juxitingdataset/供应类指标相关性直方分布图.png
+++ b/juxitingdataset/供应类指标相关性直方分布图.png
--- a/juxitingdataset/其他类指标相关性直方分布图.png
+++ b/juxitingdataset/其他类指标相关性直方分布图.png
--- a/juxitingdataset/南华工业品指数与价格散点图.png
+++ b/juxitingdataset/南华工业品指数与价格散点图.png
--- a/juxitingdataset/历史价格-预测值.png
+++ b/juxitingdataset/历史价格-预测值.png
--- a/juxitingdataset/在岸人民币汇率与价格散点图.png
+++ b/juxitingdataset/在岸人民币汇率与价格散点图.png
--- a/juxitingdataset/塑编原材料库存与价格散点图.png
+++ b/juxitingdataset/塑编原材料库存与价格散点图.png
--- a/juxitingdataset/外采丙烯制聚丙烯利润（山东）与价格散点图.png
+++ b/juxitingdataset/外采丙烯制聚丙烯利润（山东）与价格散点图.png
--- a/juxitingdataset/山东丙烯主流价与价格散点图.png
+++ b/juxitingdataset/山东丙烯主流价与价格散点图.png
--- a/juxitingdataset/布伦特-PP价差（盘面）与价格散点图.png
+++ b/juxitingdataset/布伦特-PP价差（盘面）与价格散点图.png
--- a/juxitingdataset/库存类指标相关性直方分布图.png
+++ b/juxitingdataset/库存类指标相关性直方分布图.png
--- a/juxitingdataset/指标分类相关性总和的气泡图.png
+++ b/juxitingdataset/指标分类相关性总和的气泡图.png
--- a/juxitingdataset/指标数据.csv
+++ b/juxitingdataset/指标数据.csv
--- a/juxitingdataset/指标数据偏自相关图.png
+++ b/juxitingdataset/指标数据偏自相关图.png
--- a/juxitingdataset/指标数据添加时间特征.csv
+++ b/juxitingdataset/指标数据添加时间特征.csv
--- a/juxitingdataset/指标数据自相关图.png
+++ b/juxitingdataset/指标数据自相关图.png
--- a/juxitingdataset/未填充的特征数据.csv
+++ b/juxitingdataset/未填充的特征数据.csv
--- a/juxitingdataset/模型评估.png
+++ b/juxitingdataset/模型评估.png
--- a/juxitingdataset/特征处理后的指标名称及分类.csv
+++ b/juxitingdataset/特征处理后的指标名称及分类.csv
@ -17,13 +17,10 @@
 价格,PP多空情绪强弱指数,C2305195181,周度
 价格,再生PP市场价/山东（日）,ID01460426,日度
 价格,印度粉矿：57%Fe：品牌价格：青岛港,C2307206639,日度
 价格,美国-中国PS（GPPS）价差,C2112016712,日度
 价格,塑编产成品库存,ID01230702,周度
 价格,塑编订单天数,ID01280095,周度
 价格,塑编产成品库存天数,RE00009884,周度
 价格,塑编原材料库存天数,ID01230703,周度
 价格,塑编整体开工率,ID01230748,周度
 价格,PP管材开工率,RE00009876,周度
 价格,PP无纺布开工率,RE00009875,周度
 价格,BOPP开工率,ID01230734,周度
 价格,中国PP无纺布原料库存天数,ID01367472,周度
@ -33,7 +30,6 @@
 价格,PP主力收盘价,S0203118,日度
 价格,PP粉料（山东）,Q6554151,日度
 价格,PP：拉丝：S1003：市场主流价：常州：国能榆林化工（日）,ID00164407,日度
 价格,PP：拉丝：S1003：市场主流价：东莞：宝丰能源（日）,ID00164419,日度
 价格,PP：低熔共聚：K8003：自提价：广州：宝丰能源（日）,ID00162611,日度
 价格,PP：低熔共聚：K8003：自提价：宁波：国能榆林化工（日）,ID00162667,日度
 价格,PP：拉丝：1102K：市场价：临沂：国家能源宁煤（日）,ID01385945,日度
@ -41,7 +37,6 @@
 价格,PP：透明：M26ET：出厂价：华东地区：镇海炼化（日）,ID00155667,日度
 价格,PP：透明：HC-RP340R：出厂价：华北地区：山东东明（日）,ID01384399,日度
 价格,现货价(中间价):均聚聚丙烯(PP-H):CFR远东,S5400540,日度
 价格,现货价(中间价):聚丙烯PP(纤维/注塑):CFR东南亚,S5431485,日度
 价格,现货价(中间价):聚丙烯PP(纤维/注塑):FOB西北欧,S5431506,日度
 价格,现货价(中间价):均聚聚丙烯(PP-H):FOB中东,S5400541,日度
 价格,现货价(中间价):聚丙烯PP(纤维/注塑):FAS休斯敦,S5431512,日度
@ -188,7 +183,6 @@
 库存,BOPP原料库存+产成品库存天数,C2211254027,周度
 库存,PP周度总库存,C2212057031,周度
 库存,中国BOPP原料库存天数同差,C2301198673,周度
 库存,PP库存(PDH)超季节性(3Y),C2301316785,周度
 库存,中国BOPP周度产成品库存天数（T）,84407,周度
 库存,中国BOPP周度原料库存天数（T）,84406,周度
 库存,中国再生PP样本企业周度库存,72483,周度
@ -243,7 +237,6 @@
 供应,PP管材开工率同差,C2210250903,周度
 供应,PP无纺布开工率同差,C2210258867,周度
 供应,PP日度产量（粒料+粉料）,C2210311448,日度
 供应,PP下游综合开工率/3年超季节性,C2301145692,周度
 供应,中国再生PP周度开工负荷率,72485,周度
 供应,PP无纺布生产毛利,ID01218972,日度
 供应,中国再生PP周度开工负荷率同差,C2305239026,周度
--- a/juxitingdataset/特征频度统计.csv
+++ b/juxitingdataset/特征频度统计.csv
@ -1,4 +1,4 @@
-日度(219),周度(94),84天(1)
+日度(216),周度(90),154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）,
@ -9,14 +9,12 @@ BOPP：成本：中国（日）,塑编产成品库存,
 PP无纺布生产成本（日）,塑编订单天数,
 CPP薄膜成本（日）,塑编产成品库存天数,
 PET瓶片：利润：中国（日）7DMA,塑编原材料库存天数,
-再生PP市场价/山东（日）,塑编整体开工率,
+再生PP市场价/山东（日）,PP无纺布开工率,
-印度粉矿：57%Fe：品牌价格：青岛港,PP管材开工率,
+印度粉矿：57%Fe：品牌价格：青岛港,BOPP开工率,
-美国-中国PS（GPPS）价差,PP无纺布开工率,
+PP拉丝国内现货价格,中国PP无纺布原料库存天数,
-PP拉丝国内现货价格,BOPP开工率,
+PP主力收盘价,中国PP无纺布原料库存量,
-PP主力收盘价,中国PP无纺布原料库存天数,
+PP粉料（山东）,中国PP无纺布成品库存天数,
-PP粉料（山东）,中国PP无纺布原料库存量,
+PP：拉丝：S1003：市场主流价：常州：国能榆林化工（日）,西北欧：现货价：FOB低密度聚乙烯,
 PP：拉丝：S1003：市场主流价：常州：国能榆林化工（日）,中国PP无纺布成品库存天数,
 PP：拉丝：S1003：市场主流价：东莞：宝丰能源（日）,西北欧：现货价：FOB低密度聚乙烯,
 PP：低熔共聚：K8003：自提价：广州：宝丰能源（日）,中国BOPP原料库存天数,
 PP：低熔共聚：K8003：自提价：宁波：国能榆林化工（日）,中国BOPP成品库存天数,
 PP：拉丝：1102K：市场价：临沂：国家能源宁煤（日）,中国BOPP成品库存量,
@ -24,20 +22,19 @@ PP：拉丝：1102K：市场价：青州：国家能源宁煤（日）,中国BOP
 PP：透明：M26ET：出厂价：华东地区：镇海炼化（日）,中国PP下游平均开工率,
 PP：透明：HC-RP340R：出厂价：华北地区：山东东明（日）,PP无纺布：厂内库存：中国（周）,
 现货价(中间价):均聚聚丙烯(PP-H):CFR远东,PP无纺布：生产毛利：中国（周）,
-现货价(中间价):聚丙烯PP(纤维/注塑):CFR东南亚,价差：聚乙烯与乙烯（西北欧）,
+现货价(中间价):聚丙烯PP(纤维/注塑):FOB西北欧,价差：聚乙烯与乙烯（西北欧）,
-现货价(中间价):聚丙烯PP(纤维/注塑):FOB西北欧,PP拉丝期末库存（周）,
+现货价(中间价):均聚聚丙烯(PP-H):FOB中东,PP拉丝期末库存（周）,
-现货价(中间价):均聚聚丙烯(PP-H):FOB中东,PP期末库存/广州港（周）,
+现货价(中间价):聚丙烯PP(纤维/注塑):FAS休斯敦,PP期末库存/广州港（周）,
-现货价(中间价):聚丙烯PP(纤维/注塑):FAS休斯敦,PP期末库存/宁波港（周）,
+PP主力盘中,PP期末库存/宁波港（周）,
-PP主力盘中,PP：期末库存：青岛港（周）,
+PP神华竞拍（华东）,PP：期末库存：青岛港（周）,
-PP神华竞拍（华东）,PP期末库存/上海港（周）,
+PP神华竞拍（临沂）,PP期末库存/上海港（周）,
-PP神华竞拍（临沂）,PP期末库存/天津港（周）,
+PP神华竞拍（西安）,PP期末库存/天津港（周）,
-PP神华竞拍（西安）,PP期末库存/厦门港（周）,
+DCE：工业产品：期货价格综合指数收盘价（日）,PP期末库存/厦门港（周）,
-DCE：工业产品：期货价格综合指数收盘价（日）,PP两桶油/库存（周）,
+DCE：化工产品：期货价格指数收盘价（日）,PP两桶油/库存（周）,
-DCE：化工产品：期货价格指数收盘价（日）,PP贸易商库存(钢联)同比,
+DCE：塑料：期货价格指数收盘价（日）,PP贸易商库存(钢联)同比,
-DCE：塑料：期货价格指数收盘价（日）,BOPP原料库存+产成品库存天数,
+PP.DCE收盘价,BOPP原料库存+产成品库存天数,
-PP.DCE收盘价,PP周度总库存,
+PP：低熔共聚：2500HY：市场价：临沂：国家能源宁煤（日）,PP周度总库存,
-PP：低熔共聚：2500HY：市场价：临沂：国家能源宁煤（日）,中国BOPP原料库存天数同差,
+PP：低熔共聚：K8003：出厂价：河北、鲁北：大唐内蒙多伦（日）,中国BOPP原料库存天数同差,
 PP：低熔共聚：K8003：出厂价：河北、鲁北：大唐内蒙多伦（日）,PP库存(PDH)超季节性(3Y),
 PP：低熔共聚：EPS30R：市场价：临沂：榆能化（日）,中国BOPP周度产成品库存天数（T）,
 PP：低熔共聚：K8003：出厂价：临沂：宝丰能源（日）,中国BOPP周度原料库存天数（T）,
 PP：低熔共聚：K8003：出厂价：常州：宝丰能源（日）,中国再生PP样本企业周度库存,
@ -71,28 +68,28 @@ PP：薄壁注塑：TM6000H：出厂价：华南地区：福建联合石化（
 PP：透明：PPR-B10：出厂价：华中地区：中原中石化（日）,PP下游开工/PE下游开工,
 PP：透明：PPR-MT25：出厂价：华中地区：中原中石化（日）,PP管材开工率同差,
 PP：透明：PPR-MT75N：出厂价：华中地区：中原中石化（日）,PP无纺布开工率同差,
-PP：无规共聚：HC-M700B：出厂价：华北地区：山东东明（日）,PP下游综合开工率/3年超季节性,
+PP：无规共聚：HC-M700B：出厂价：华北地区：山东东明（日）,中国再生PP周度开工负荷率,
-热水管：PA14D：市场价：青州：大庆炼化（日）,中国再生PP周度开工负荷率,
+热水管：PA14D：市场价：青州：大庆炼化（日）,中国再生PP周度开工负荷率同差,
-CPP：二元共聚膜：DY-W0723F：市场主流价：天津：独山子石化（日）,中国再生PP周度开工负荷率同差,
+CPP：二元共聚膜：DY-W0723F：市场主流价：天津：独山子石化（日）,PP粉料开工率（4WMA）,
-PP：透明：R3080T：出厂价：华东地区：浙江鸿基（日）,PP粉料开工率（4WMA）,
+PP：透明：R3080T：出厂价：华东地区：浙江鸿基（日）,PP检修减损量,
-PP：透明：M08ETN：出厂价：华东地区：镇海炼化（日）,PP检修减损量,
+PP：透明：M08ETN：出厂价：华东地区：镇海炼化（日）,PP下游综合开工率（少注塑&CPP）,
-PP：透明：R3260T：出厂价：华东地区：浙江鸿基（日）,PP下游综合开工率（少注塑&CPP）,
+PP：透明：R3260T：出厂价：华东地区：浙江鸿基（日）,BOPP开工率同差,
-热水管：YPR-503：出厂价：华东地区：扬子石化（日）,BOPP开工率同差,
+热水管：YPR-503：出厂价：华东地区：扬子石化（日）,BOPP开工率（4WMA）,
-热水管：PA14D：出厂价：华东地区：大庆炼化（日）,BOPP开工率（4WMA）,
+热水管：PA14D：出厂价：华东地区：大庆炼化（日）,PP粉料开工率,
-CPP：二元共聚膜：F800EDF：出厂价：华东地区：上海石化（日）,PP粉料开工率,
+CPP：二元共聚膜：F800EDF：出厂价：华东地区：上海石化（日）,PP周度检修率,
-CPP：二元共聚膜：F08EC：出厂价：华东地区：镇海炼化（日）,PP周度检修率,
+CPP：二元共聚膜：F08EC：出厂价：华东地区：镇海炼化（日）,PP周度产量周环差,
-PP：透明：HT9025NX：出厂价：华南地区：茂名石化（日）,PP周度产量周环差,
+PP：透明：HT9025NX：出厂价：华南地区：茂名石化（日）,PP周度产量同比,
-PP：透明：HT9025ZK：出厂价：华南地区：中科炼化（日）,PP周度产量同比,
+PP：透明：HT9025ZK：出厂价：华南地区：中科炼化（日）,PP下游成品库存天数,
-PP：透明：HT9025NX：市场主流价：广州：中石化茂名（日）,PP下游成品库存天数,
+PP：透明：HT9025NX：市场主流价：广州：中石化茂名（日）,PP部分下游订单天数,
-热水管：T4401：出厂价：华南地区：茂名石化（日）,PP部分下游订单天数,
+热水管：T4401：出厂价：华南地区：茂名石化（日）,中国BOPP订单天数同差,
-热水管：PPR-4220：出厂价：华南地区：广州石化（日）,中国BOPP订单天数同差,
+热水管：PPR-4220：出厂价：华南地区：广州石化（日）,中国BOPP订单天数/4WMA,
-PP：BOPP：1103K：出厂价：华北地区：国家能源宁煤（日）,中国BOPP订单天数/4WMA,
+PP：BOPP：1103K：出厂价：华北地区：国家能源宁煤（日）,BOPP完工订单工作量（周）,
-PP：BOPP：PPH-FL03-S：出厂价：华北地区：青岛炼化（日）,BOPP完工订单工作量（周）,
+PP：BOPP：PPH-FL03-S：出厂价：华北地区：青岛炼化（日）,中国再生PP周度样本成交量,
-PP：BOPP：1103K：出厂价：华东地区：国家能源宁煤（日）,中国再生PP周度样本成交量,
+PP：BOPP：1103K：出厂价：华东地区：国家能源宁煤（日）,BOPP开工率超季节性/3年,
-PP：BOPP：F03BT：出厂价：华东地区：镇海炼化（日）,BOPP开工率超季节性/3年,
+PP：BOPP：F03BT：出厂价：华东地区：镇海炼化（日）,中国BOPP成品库存量同差,
-PP：BOPP：1103K：出厂价：华南地区：宁夏神华宁煤（日）,中国BOPP成品库存量同差,
+PP：BOPP：1103K：出厂价：华南地区：宁夏神华宁煤（日）,中国CPP订单天数,
-PP：BOPP：L5D98：出厂价：华南地区：广东石化（日）,中国CPP订单天数,
+PP：BOPP：L5D98：出厂价：华南地区：广东石化（日）,BOPP 订单-成品天数,
-PP：BOPP：PPH-F03D：出厂价：华南地区：海南炼化装置一（日）,BOPP 订单-成品天数,
+PP：BOPP：PPH-F03D：出厂价：华南地区：海南炼化装置一（日）,,
 CPP：二元共聚膜：PPR-F08M-S：出厂价：华南地区：茂名石化（日）,,
 CPP：二元共聚膜：PPR-F08-S：出厂价：华南地区：茂名石化（日）,,
 BOPP：12μ光膜：出厂价：华北地区：凯达包装（日）,,
--- 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312个,日度(219),周度(94),84天(1), 详看 附1、特征列表
+特征信息：总共有305个,日度(216),周度(90),154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
--- a/juxitingdataset/美元指数与价格散点图.png
+++ b/juxitingdataset/美元指数与价格散点图.png
--- a/juxitingdataset/聚丙烯出口利润与价格散点图.png
+++ b/juxitingdataset/聚丙烯出口利润与价格散点图.png
--- a/juxitingdataset/聚丙烯进口利润与价格散点图.png
+++ b/juxitingdataset/聚丙烯进口利润与价格散点图.png
--- a/juxitingdataset/薄壁-拉丝价差（华北）与价格散点图.png
+++ b/juxitingdataset/薄壁-拉丝价差（华北）与价格散点图.png
--- a/juxitingdataset/进出口类指标相关性直方分布图.png
+++ b/juxitingdataset/进出口类指标相关性直方分布图.png
--- a/juxitingdataset/需求类指标相关性直方分布图.png
+++ b/juxitingdataset/需求类指标相关性直方分布图.png
--- a/juxitingdataset/预测值与真实值对比图.png
+++ b/juxitingdataset/预测值与真实值对比图.png
--- a/juxitingdataset/预测值表格.png
+++ b/juxitingdataset/预测值表格.png
--- a/juxitingdataset/黄金连1合约与价格散点图.png
+++ b/juxitingdataset/黄金连1合约与价格散点图.png
--- a/logs/pricepredict.log.1
+++ b/logs/pricepredict.log.1
--- a/logs/pricepredict.log.3
+++ b/logs/pricepredict.log.3
--- a/logs/pricepredict.log.5
+++ b/logs/pricepredict.log.5
--- a/main_juxiting.py
+++ b/main_juxiting.py
@ -110,25 +110,25 @@ def predict_main():
    row,col = df.shape
    now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
-    # ex_Model(df,
+    ex_Model(df,
-    #         horizon=horizon,
+            horizon=horizon,
-    #         input_size=input_size,
+            input_size=input_size,
-    #         train_steps=train_steps,
+            train_steps=train_steps,
-    #         val_check_steps=val_check_steps,
+            val_check_steps=val_check_steps,
-    #         early_stop_patience_steps=early_stop_patience_steps,
+            early_stop_patience_steps=early_stop_patience_steps,
-    #         is_debug=is_debug,
+            is_debug=is_debug,
-    #         dataset=dataset,
+            dataset=dataset,
-    #         is_train=is_train,
+            is_train=is_train,
-    #         is_fivemodels=is_fivemodels,
+            is_fivemodels=is_fivemodels,
-    #         val_size=val_size,
+            val_size=val_size,
-    #         test_size=test_size,
+            test_size=test_size,
-    #         settings=settings,
+            settings=settings,
-    #         now=now,
+            now=now,
-    #         etadata = etadata,
+            etadata = etadata,
-    #         modelsindex = modelsindex,
+            modelsindex = modelsindex,
-    #         data = data,
+            data = data,
-    #         is_eta=is_eta,
+            is_eta=is_eta,
-    #         )
+            )
    logger.info('模型训练完成')
--- a/models/nerulforcastmodels.py
+++ b/models/nerulforcastmodels.py
@ -486,6 +486,9 @@ def model_losss(sqlitedb):
 # 计算预测评估指数
 def model_losss_juxiting(sqlitedb):
    global dataset
    most_model = [sqlitedb.select_data('most_model',columns=['most_common_model'],order_by='ds desc',limit=1).values[0][0]]
    most_model_name = most_model[0]
    # 预测数据处理 predict
    df_combined = loadcsv(os.path.join(dataset,"cross_validation.csv"))  
    df_combined = dateConvert(df_combined)
@ -535,26 +538,6 @@ def model_losss_juxiting(sqlitedb):
        f.write(','.join(modelnames) + '\n')
    # 根据最接近真实值的预测模型计算波动率，得到在波动率范围内的预测值确定通道边界
    best_models = pd.read_csv(os.path.join(dataset,'best_modelnames.txt'),header=None).values.flatten().tolist()
    def find_min_max_within_quantile(row):
        row = row[best_models]
        q10 = row.min()
        q90 = row.max()
        # 获取 row行最大最小值模型名称
        min_model = row[row == q10].idxmin()
        max_model = row[row == q90].idxmin()
        # # 判断flot值是否为空值
        # if pd.isna(q10) or pd.isna(q90):
        return pd.Series([q10, q90,min_model,max_model], index=['min_within_quantile','max_within_quantile','min_model','max_model'])
    # 遍历行
    df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)
    df_combined = df_combined.round(4)
    print(df_combined3)
    # # 根据真实值y确定最大最小值,去掉最高最低的预测值
    # import heapq          # 使用堆来找到最大和最小的值
@ -728,12 +711,13 @@ def model_losss_juxiting(sqlitedb):
    # df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)
    # 去除有空值的行
-    df_combined3.dropna(inplace=True)
+    # df_combined3.dropna(inplace=True)
-    # 保存到数据库
+    # # 保存到数据库
-    df_combined3.to_sql('testandpredict_groupby', sqlitedb.connection, if_exists='replace', index=False)
+    # df_combined3.to_sql('testandpredict_groupby', sqlitedb.connection, if_exists='replace', index=False)
-    df_combined3.to_csv(os.path.join(dataset,"testandpredict_groupby.csv"),index=False)
+    # df_combined3.to_csv(os.path.join(dataset,"testandpredict_groupby.csv"),index=False)
    # 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
    # 预测值与真实值对比图
    plt.rcParams['font.sans-serif'] = ['SimHei']
@ -756,7 +740,7 @@ def model_losss_juxiting(sqlitedb):
    df_predict  = loadcsv(os.path.join(dataset,'predict.csv'))
    df_predict.drop('unique_id',inplace=True,axis=1)
    df_predict.dropna(axis=1,inplace=True)
-
+    df_predict2 = df_predict.copy()
    try:
        df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y-%m-%d')
    except ValueError :
@ -765,46 +749,23 @@ def model_losss_juxiting(sqlitedb):
    # 取第一行数据存储到数据库中
    first_row = df_predict.head(1)
    first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00')
    # 将预测结果保存到数据库
    if not sqlitedb.check_table_exists('trueandpredict'):
        first_row.to_sql('trueandpredict',sqlitedb.connection,index=False)
    else:
        for col in first_row.columns:
                sqlitedb.add_column_if_not_exists('trueandpredict',col,'TEXT')
        for row in first_row.itertuples(index=False):
            row_dict = row._asdict()
            columns=row_dict.keys()
            check_query = sqlitedb.select_data('trueandpredict',where_condition = f"ds = '{row.ds}'")
            if len(check_query) > 0:
                set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
                sqlitedb.update_data('trueandpredict',set_clause,where_condition = f"ds = '{row.ds}'")
                continue
            sqlitedb.insert_data('trueandpredict',tuple(row_dict.values()),columns=columns)
-    
+    # # 将预测结果保存到数据库
-
+    df_combined3 = pd.concat([df_combined3, df_predict]).reset_index(drop=True)
-    # 最多频率的模型名称
+    # # 判断 df 的数值列转为float
-    min_model_max_frequency_model = df_combined3['min_model'].tail(20).value_counts().idxmax()
+    for col in df_combined3.columns:
-    max_model_max_frequency_model = df_combined3['max_model'].tail(20).value_counts().idxmax()
+        try:
-    if min_model_max_frequency_model == max_model_max_frequency_model:
+            if col != 'ds':
-        # 取20天第二多的模型
+                df_combined3[col] = df_combined3[col].astype(float)
-        max_model_max_frequency_model = df_combined3['max_model'].tail(20).value_counts().nlargest(2).index[1]
+                df_combined3[col] = df_combined3[col].round(2)
-
+        except ValueError:
-    df_predict['min_model'] = min_model_max_frequency_model
+            pass
-    df_predict['max_model'] = max_model_max_frequency_model
+    df_combined3.to_csv(os.path.join(dataset,"testandpredict_groupby.csv"),index=False)
-    df_predict['min_within_quantile'] = df_predict[min_model_max_frequency_model]
+    # # 判断表存在
    df_predict['max_within_quantile'] = df_predict[max_model_max_frequency_model]
    df_predict2 = df_predict.copy()
    df_predict2['ds'] = df_predict2['ds'].dt.strftime('%Y-%m-%d 00:00:00')
    # 将预测结果保存到数据库
    # 判断表存在
    if not sqlitedb.check_table_exists('testandpredict_groupby'):
-        df_predict2.to_sql('testandpredict_groupby',sqlitedb.connection,index=False)
+        df_combined3.to_sql('testandpredict_groupby',sqlitedb.connection,index=False)
    else:
-        for row in df_predict2.itertuples(index=False):
+        for row in df_combined3.itertuples(index=False):
            row_dict = row._asdict()
            check_query = sqlitedb.select_data('testandpredict_groupby',where_condition = f"ds = '{row.ds}'")
            if len(check_query) > 0:
@ -813,62 +774,174 @@ def model_losss_juxiting(sqlitedb):
                continue
            sqlitedb.insert_data('testandpredict_groupby',tuple(row_dict.values()),columns=row_dict.keys())
-    
+    ten_models = allmodelnames
-
+    # 计算每个模型的方差
-    # 计算每个预测值与真实值之间的偏差率
+    variances = df_combined3[ten_models].var()
-    for model in allmodelnames:
+    # 找到方差最大的模型
-        df_combined3[f'{model}_abs_error_rate'] = abs(df_combined3['y'] - df_combined3[model]) / df_combined3['y']
+    max_variance_model = variances.idxmax()
-
+    # 打印方差最大的模型
-    # 获取每行对应的最小偏差率值
+    print("方差最大的模型是：", max_variance_model)
-    min_abs_error_rate_values = df_combined3.apply(lambda row: row[[f'{model}_abs_error_rate' for model in allmodelnames]].min(), axis=1)
+    # 去掉方差最大的模型
-    # 获取每行对应的最小偏差率值对应的列名
+    df_combined3 = df_combined3.drop(columns=[max_variance_model])
-    min_abs_error_rate_column_name = df_combined3.apply(lambda row: row[[f'{model}_abs_error_rate' for model in allmodelnames]].idxmin(), axis=1)
+    if max_variance_model in allmodelnames:
-    # 将列名索引转换为列名
+        allmodelnames.remove(max_variance_model)
-    min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])
+    df_combined3['min'] = df_combined3[allmodelnames].min(axis=1)
-    # 获取最小偏差率对应的模型的预测值
+    df_combined3['max'] = df_combined3[allmodelnames].max(axis=1)
-    min_abs_error_rate_predictions = df_combined3.apply(lambda row: row[min_abs_error_rate_column_name[row.name]], axis=1)
+    print(df_combined3[['min','max']])
    # 将最小偏差率对应的模型的预测值添加到DataFrame中
    df_combined3['min_abs_error_rate_prediction'] = min_abs_error_rate_predictions
    df_combined3['min_abs_error_rate_column_name'] = min_abs_error_rate_column_name
    df_combined3 = pd.concat([df_combined3, df_predict]).reset_index(drop=True)
    # 判断 df 的数值列转为float
    for col in df_combined3.columns:
        try:
            if col != 'ds':
                df_combined3[col] = df_combined3[col].astype(float)
                df_combined3[col] = df_combined3[col].round(2)
        except ValueError:
            pass
    df_combined3.to_csv(os.path.join(dataset,"df_combined3.csv"),index=False) 
    # 历史价格+预测价格
    df_combined3 = df_combined3[-50:] # 取50个数据点画图
    # 历史价格
    plt.figure(figsize=(20, 10))
-    plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值')
+    plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值',marker='o')
-    # 颜色填充
+    plt.plot(df_combined3['ds'], df_combined3[most_model], label=most_model_name)
-    plt.fill_between(df_combined3['ds'], df_combined3['min_within_quantile'], df_combined3['max_within_quantile'], alpha=0.2)
+    plt.fill_between(df_combined3['ds'], df_combined3['min'], df_combined3['max'], alpha=0.2)
    # plt.plot(df_combined3['ds'], df_combined3['min_abs_error_rate_prediction'], label='最小绝对误差', linestyle='--', color='orange')
    # 网格
    plt.grid(True)
    # 显示历史值
    for i, j in zip(df_combined3['ds'], df_combined3['y']):
        plt.text(i, j, str(j), ha='center', va='bottom')
    # 数据库查询最佳模型名称
    most_model = [sqlitedb.select_data('most_model',columns=['most_common_model'],order_by='ds desc',limit=1).values[0][0]]
    for model in most_model:
        plt.plot(df_combined3['ds'], df_combined3[model], label=model,marker='o')
    # 当前日期画竖虚线
    plt.axvline(x=df_combined3['ds'].iloc[-horizon], color='r', linestyle='--')
    plt.legend()
    plt.xlabel('日期')
    plt.ylabel('价格')
    # # 显示历史值
    for i, j in zip(df_combined3['ds'][:-5], df_combined3['y'][:-5]):
        plt.text(i, j, str(j), ha='center', va='bottom')
    plt.savefig(os.path.join(dataset,'历史价格-预测值.png'), bbox_inches='tight')
    plt.close()
    # 预测值与真实值对比图
    plt.rcParams['font.sans-serif'] = ['SimHei']
    plt.figure(figsize=(15, 10))
    # 设置有5个子图的画布
    for n,model in enumerate(modelnames[:5]):
        plt.subplot(3, 2, n+1)
        plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值')
        plt.plot(df_combined3['ds'], df_combined3[model], label=model)
        plt.legend()
        plt.xlabel('日期')
        plt.ylabel('价格')
        plt.title(model+'拟合')
    plt.subplots_adjust(hspace=0.5)
    plt.savefig(os.path.join(dataset,'预测值与真实值对比图.png'), bbox_inches='tight')
    plt.close()
    # # 历史数据+预测数据
    # # 拼接未来时间预测
    # df_predict  = loadcsv(os.path.join(dataset,'predict.csv'))
    # df_predict.drop('unique_id',inplace=True,axis=1)
    # df_predict.dropna(axis=1,inplace=True)
    # try:
    #     df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y-%m-%d')
    # except ValueError :
    #     df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y/%m/%d')
    # # 取第一行数据存储到数据库中
    # first_row = df_predict.head(1)
    # first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00')
    # # 将预测结果保存到数据库
    # if not sqlitedb.check_table_exists('trueandpredict'):
    #     first_row.to_sql('trueandpredict',sqlitedb.connection,index=False)
    # else:
    #     for col in first_row.columns:
    #             sqlitedb.add_column_if_not_exists('trueandpredict',col,'TEXT')
    #     for row in first_row.itertuples(index=False):
    #         row_dict = row._asdict()
    #         columns=row_dict.keys()
    #         check_query = sqlitedb.select_data('trueandpredict',where_condition = f"ds = '{row.ds}'")
    #         if len(check_query) > 0:
    #             set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
    #             sqlitedb.update_data('trueandpredict',set_clause,where_condition = f"ds = '{row.ds}'")
    #             continue
    #         sqlitedb.insert_data('trueandpredict',tuple(row_dict.values()),columns=columns)
    # # 最多频率的模型名称
    # min_model_max_frequency_model = df_combined3['min_model'].tail(20).value_counts().idxmax()
    # max_model_max_frequency_model = df_combined3['max_model'].tail(20).value_counts().idxmax()
    # if min_model_max_frequency_model == max_model_max_frequency_model:
    #     # 取20天第二多的模型
    #     max_model_max_frequency_model = df_combined3['max_model'].tail(20).value_counts().nlargest(2).index[1]
    # df_predict['min_model'] = min_model_max_frequency_model
    # df_predict['max_model'] = max_model_max_frequency_model
    # df_predict['min_within_quantile'] = df_predict[min_model_max_frequency_model]
    # df_predict['max_within_quantile'] = df_predict[max_model_max_frequency_model]
    # df_predict2 = df_predict.copy()
    # df_predict2['ds'] = df_predict2['ds'].dt.strftime('%Y-%m-%d 00:00:00')
    # # 将预测结果保存到数据库
    # # 判断表存在
    # if not sqlitedb.check_table_exists('testandpredict_groupby'):
    #     df_predict2.to_sql('testandpredict_groupby',sqlitedb.connection,index=False)
    # else:
    #     for row in df_predict2.itertuples(index=False):
    #         row_dict = row._asdict()
    #         check_query = sqlitedb.select_data('testandpredict_groupby',where_condition = f"ds = '{row.ds}'")
    #         if len(check_query) > 0:
    #             set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
    #             sqlitedb.update_data('testandpredict_groupby',set_clause,where_condition = f"ds = '{row.ds}'")
    #             continue
    #         sqlitedb.insert_data('testandpredict_groupby',tuple(row_dict.values()),columns=row_dict.keys())
    # # 计算每个预测值与真实值之间的偏差率
    # for model in allmodelnames:
    #     df_combined3[f'{model}_abs_error_rate'] = abs(df_combined3['y'] - df_combined3[model]) / df_combined3['y']
    # # 获取每行对应的最小偏差率值
    # min_abs_error_rate_values = df_combined3.apply(lambda row: row[[f'{model}_abs_error_rate' for model in allmodelnames]].min(), axis=1)
    # # 获取每行对应的最小偏差率值对应的列名
    # min_abs_error_rate_column_name = df_combined3.apply(lambda row: row[[f'{model}_abs_error_rate' for model in allmodelnames]].idxmin(), axis=1)
    # # 将列名索引转换为列名
    # min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])
    # # 获取最小偏差率对应的模型的预测值
    # min_abs_error_rate_predictions = df_combined3.apply(lambda row: row[min_abs_error_rate_column_name[row.name]], axis=1)
    # # 将最小偏差率对应的模型的预测值添加到DataFrame中
    # df_combined3['min_abs_error_rate_prediction'] = min_abs_error_rate_predictions
    # df_combined3['min_abs_error_rate_column_name'] = min_abs_error_rate_column_name
    # df_combined3 = pd.concat([df_combined3, df_predict]).reset_index(drop=True)
    # # 判断 df 的数值列转为float
    # for col in df_combined3.columns:
    #     try:
    #         if col != 'ds':
    #             df_combined3[col] = df_combined3[col].astype(float)
    #             df_combined3[col] = df_combined3[col].round(2)
    #     except ValueError:
    #         pass
    # df_combined3.to_csv(os.path.join(dataset,"df_combined3.csv"),index=False) 
    #  # 历史价格+预测价格
    # df_combined3 = df_combined3[-50:] # 取50个数据点画图
    # # 历史价格
    # plt.figure(figsize=(20, 10))
    # plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值')
    # # 颜色填充
    # plt.fill_between(df_combined3['ds'], df_combined3['min_within_quantile'], df_combined3['max_within_quantile'], alpha=0.2)
    # # plt.plot(df_combined3['ds'], df_combined3['min_abs_error_rate_prediction'], label='最小绝对误差', linestyle='--', color='orange')
    # # 网格
    # plt.grid(True)
    # # 显示历史值
    # for i, j in zip(df_combined3['ds'], df_combined3['y']):
    #     plt.text(i, j, str(j), ha='center', va='bottom')
    # # 数据库查询最佳模型名称
    # most_model = [sqlitedb.select_data('most_model',columns=['most_common_model'],order_by='ds desc',limit=1).values[0][0]]
    # for model in most_model:
    #     plt.plot(df_combined3['ds'], df_combined3[model], label=model,marker='o')
    # # 当前日期画竖虚线
    # plt.axvline(x=df_combined3['ds'].iloc[-horizon], color='r', linestyle='--')
    # plt.legend()
    # plt.xlabel('日期')
    # plt.ylabel('价格')
    # plt.savefig(os.path.join(dataset,'历史价格-预测值.png'), bbox_inches='tight')
    # plt.close()
    # 预测值表格
    fig, ax = plt.subplots(figsize=(20, 6))
    ax.axis('off')  # 关闭坐标轴
@ -886,7 +959,6 @@ def model_losss_juxiting(sqlitedb):
    # 设置表格样式，列数据最小的用绿色标识
    plt.savefig(os.path.join(dataset,'预测值表格.png'), bbox_inches='tight')
    plt.close()
    # plt.show()
    # 可视化评估结果
    plt.rcParams['font.sans-serif'] = ['SimHei']
--- a/聚烯烃预测值绘图调试.ipynb
+++ b/聚烯烃预测值绘图调试.ipynb
@ -0,0 +1,571 @@
 {
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7fadc60c-d710-4b8c-89cd-1d889ece1eaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "从eta获取数据...\n",
      "从eta获取数据...\n",
      "['ID01385938', 'lmcads03 lme comdty', 'GC1 COMB Comdty', 'C2404171822', 'dxy curncy', 'S5443199 ', 'S5479800', 'S5443108', 'H7358586', 'LC3FM1 INDEX', 'CNY REGN Curncy', 's0105897', 'M0067419', 'M0066351', 'S0266372', 'S0266438', 'S0266506']\n",
      "['ID01385938', 'lmcads03 lme comdty', 'GC1 COMB Comdty', 'C2404171822', 'dxy curncy', 'S5443199 ', 'S5479800', 'S5443108', 'H7358586', 'LC3FM1 INDEX', 'CNY REGN Curncy', 's0105897', 'M0067419', 'M0066351', 'S0266372', 'S0266438', 'S0266506']\n",
      "ID01385938\n",
      "ID01385938\n",
      "lmcads03 lme comdty\n",
      "lmcads03 lme comdty\n",
      "GC1 COMB Comdty\n",
      "GC1 COMB Comdty\n",
      "C2404171822\n",
      "C2404171822\n",
      "dxy curncy\n",
      "dxy curncy\n",
      "S5443199 \n",
      "S5443199 \n",
      "S5479800\n",
      "S5479800\n",
      "S5443108\n",
      "S5443108\n",
      "H7358586\n",
      "H7358586\n",
      "LC3FM1 INDEX\n",
      "LC3FM1 INDEX\n",
      "CNY REGN Curncy\n",
      "CNY REGN Curncy\n",
      "s0105897\n",
      "s0105897\n",
      "M0067419\n",
      "M0067419\n",
      "M0066351\n",
      "M0066351\n",
      "S0266372\n",
      "S0266372\n",
      "S0266438\n",
      "S0266438\n",
      "S0266506\n",
      "S0266506\n",
      "         date  PP：拉丝：1102K：市场价：青州：国家能源宁煤（日）   LME铜价  黄金连1合约  Brent-WTI  \\\n",
      "0  2024-11-19                        7390.0     NaN  2634.7       4.12   \n",
      "1  2024-11-18                        7380.0  9072.5  2614.6       4.14   \n",
      "2  2024-11-15                        7380.0  9002.5  2570.1       4.02   \n",
      "3  2024-11-14                        7380.0  8990.0  2572.9       3.86   \n",
      "4  2024-11-13                        7380.0  9047.0  2586.5       3.85   \n",
      "\n",
      "      美元指数  甲醇鲁南价格  甲醇太仓港口价格  山东丙烯主流价  丙烷（山东）  FEI丙烷 M1  在岸人民币汇率  南华工业品指数  \\\n",
      "0  106.206     NaN       NaN      NaN     NaN    615.08   7.2387      NaN   \n",
      "1  106.275  2340.0       NaN   6850.0     NaN    606.60   7.2320  3727.05   \n",
      "2  106.687  2310.0       NaN   6930.0     NaN    611.69   7.2294  3708.14   \n",
      "3  106.673  2310.0    2472.0   6945.0     NaN    618.61   7.2271  3739.76   \n",
      "4  106.481  2310.0    2480.0   6800.0     NaN    621.88   7.2340  3772.43   \n",
      "\n",
      "   PVC期货主力  PE期货收盘价  PP连续-1月  PP连续-5月  PP连续-9月  \n",
      "0      NaN      NaN      NaN      NaN      NaN  \n",
      "1   5284.0      NaN      NaN      NaN      NaN  \n",
      "2   5282.0      NaN      NaN      NaN      NaN  \n",
      "3   5310.0      NaN      NaN      NaN      NaN  \n",
      "4   5347.0      NaN      NaN      NaN      NaN  \n",
      "         date  PP：拉丝：1102K：市场价：青州：国家能源宁煤（日）   LME铜价  黄金连1合约  Brent-WTI  \\\n",
      "0  2024-11-19                        7390.0     NaN  2634.7       4.12   \n",
      "1  2024-11-18                        7380.0  9072.5  2614.6       4.14   \n",
      "2  2024-11-15                        7380.0  9002.5  2570.1       4.02   \n",
      "3  2024-11-14                        7380.0  8990.0  2572.9       3.86   \n",
      "4  2024-11-13                        7380.0  9047.0  2586.5       3.85   \n",
      "\n",
      "      美元指数  甲醇鲁南价格  甲醇太仓港口价格  山东丙烯主流价  丙烷（山东）  FEI丙烷 M1  在岸人民币汇率  南华工业品指数  \\\n",
      "0  106.206     NaN       NaN      NaN     NaN    615.08   7.2387      NaN   \n",
      "1  106.275  2340.0       NaN   6850.0     NaN    606.60   7.2320  3727.05   \n",
      "2  106.687  2310.0       NaN   6930.0     NaN    611.69   7.2294  3708.14   \n",
      "3  106.673  2310.0    2472.0   6945.0     NaN    618.61   7.2271  3739.76   \n",
      "4  106.481  2310.0    2480.0   6800.0     NaN    621.88   7.2340  3772.43   \n",
      "\n",
      "   PVC期货主力  PE期货收盘价  PP连续-1月  PP连续-5月  PP连续-9月  \n",
      "0      NaN      NaN      NaN      NaN      NaN  \n",
      "1   5284.0      NaN      NaN      NaN      NaN  \n",
      "2   5282.0      NaN      NaN      NaN      NaN  \n",
      "3   5310.0      NaN      NaN      NaN      NaN  \n",
      "4   5347.0      NaN      NaN      NaN      NaN  \n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "\"None of [Index(['PP：拉丝：HP550J：市场价：青岛：金能化学（日）'], dtype='object')] are in the [columns]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[12], line 41\u001b[0m\n\u001b[0;32m     38\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     40\u001b[0m     \u001b[38;5;66;03m# 数据处理\u001b[39;00m\n\u001b[1;32m---> 41\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\u001b[0;32m     43\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     44\u001b[0m     logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m读取本地数据：\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39mos\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(dataset,data_set))\n",
      "File \u001b[1;32mD:\\liurui\\dev\\code\\PriceForecast\\lib\\dataread.py:635\u001b[0m, in \u001b[0;36mdatachuli_juxiting\u001b[1;34m(df_zhibiaoshuju, df_zhibiaoliebiao, datecol, end_time, y, dataset, delweekenday, add_kdj, is_timefurture)\u001b[0m\n\u001b[0;32m    632\u001b[0m df\u001b[38;5;241m.\u001b[39mrename(columns\u001b[38;5;241m=\u001b[39m{datecol:\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mds\u001b[39m\u001b[38;5;124m'\u001b[39m},inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m    634\u001b[0m \u001b[38;5;66;03m# 指定列统一减少数值\u001b[39;00m\n\u001b[1;32m--> 635\u001b[0m df[offsite_col] \u001b[38;5;241m=\u001b[39m df[offsite_col]\u001b[38;5;241m-\u001b[39moffsite\n\u001b[0;32m    636\u001b[0m \u001b[38;5;66;03m# 预测列为avg_cols的均值\u001b[39;00m\n\u001b[0;32m    637\u001b[0m df[y] \u001b[38;5;241m=\u001b[39m df[avg_cols]\u001b[38;5;241m.\u001b[39mmean(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:3899\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3897\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[0;32m   3898\u001b[0m         key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[1;32m-> 3899\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39m_get_indexer_strict(key, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m   3901\u001b[0m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[0;32m   3902\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n",
      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6115\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[1;34m(self, key, axis_name)\u001b[0m\n\u001b[0;32m   6112\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   6113\u001b[0m     keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[1;32m-> 6115\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_if_missing(keyarr, indexer, axis_name)\n\u001b[0;32m   6117\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[0;32m   6118\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[0;32m   6119\u001b[0m     \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n",
      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6176\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[1;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[0;32m   6174\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m use_interval_msg:\n\u001b[0;32m   6175\u001b[0m         key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[1;32m-> 6176\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   6178\u001b[0m not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[0;32m   6179\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mKeyError\u001b[0m: \"None of [Index(['PP：拉丝：HP550J：市场价：青岛：金能化学（日）'], dtype='object')] are in the [columns]\""
     ]
    }
   ],
   "source": [
    "# 读取配置\n",
    "from lib import *\n",
    "from lib.dataread import *\n",
    "from lib.tools import *\n",
    "from models.nerulforcastmodels import ex_Model,model_losss,brent_export_pdf,tansuanli_export_pdf,pp_export_pdf,model_losss_juxiting\n",
    "\n",
    "import glob\n",
    "import torch\n",
    "torch.set_float32_matmul_precision(\"high\")\n",
    "\n",
    "sqlitedb = SQLiteHandler(db_name) \n",
    "sqlitedb.connect()\n",
    "\n",
    "signature = BinanceAPI(APPID, SECRET)\n",
    "etadata = EtaReader(signature=signature,\n",
    "                        classifylisturl = classifylisturl,\n",
    "                        classifyidlisturl=classifyidlisturl,\n",
    "                        edbcodedataurl=edbcodedataurl,\n",
    "                        edbcodelist=edbcodelist,\n",
    "                        edbdatapushurl=edbdatapushurl,\n",
    "                        edbdeleteurl=edbdeleteurl,\n",
    "                        edbbusinessurl=edbbusinessurl\n",
    "                        )\n",
    "# 获取数据\n",
    "if is_eta:\n",
    "    # eta数据\n",
    "    logger.info('从eta获取数据...')\n",
    "    signature = BinanceAPI(APPID, SECRET)\n",
    "    etadata = EtaReader(signature=signature,\n",
    "                                classifylisturl = classifylisturl,\n",
    "                                classifyidlisturl=classifyidlisturl,\n",
    "                                edbcodedataurl=edbcodedataurl,\n",
    "                                edbcodelist=edbcodelist,\n",
    "                                edbdatapushurl=edbdatapushurl,\n",
    "                                edbdeleteurl=edbdeleteurl,\n",
    "                                edbbusinessurl=edbbusinessurl,\n",
    "                                )\n",
    "    df_zhibiaoshuju,df_zhibiaoliebiao =  etadata.get_eta_api_pp_data(data_set=data_set,dataset=dataset)  # 原始数据，未处理\n",
    "\n",
    "    # 数据处理\n",
    "    df = datachuli_juxiting(df_zhibiaoshuju,df_zhibiaoliebiao,y = y,dataset=dataset,add_kdj=add_kdj,is_timefurture=is_timefurture,end_time=end_time) \n",
    "\n",
    "else:\n",
    "    logger.info('读取本地数据：'+os.path.join(dataset,data_set))\n",
    "    df = getdata_juxiting(filename=os.path.join(dataset,data_set),y=y,dataset=dataset,add_kdj=add_kdj,is_timefurture=is_timefurture,end_time=end_time)  # 原始数据，未处理\n",
    "\n",
    "# 更改预测列名称\n",
    "df.rename(columns={y:'y'},inplace=True)\n",
    "    \n",
    "if is_edbnamelist:\n",
    "    df = df[edbnamelist]     \n",
    "df.to_csv(os.path.join(dataset,'指标数据.csv'), index=False)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae059224-976c-4839-b455-f81da7f25179",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存最新日期的y值到数据库\n",
    "# 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",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abb597fc-c5f3-4d76-8099-5eff358cb634",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "# 判断当前日期是不是周一\n",
    "is_weekday = datetime.datetime.now().weekday() == 1\n",
    "if is_weekday:\n",
    "    logger.info('今天是周一，更新预测模型')\n",
    "    # 计算最近20天预测残差最低的模型名称\n",
    "\n",
    "    model_results = sqlitedb.select_data('trueandpredict',order_by = \"ds DESC\",limit = \"20\")\n",
    "    # 删除空值率为40%以上的列\n",
    "    print(model_results.shape)\n",
    "    model_results = model_results.dropna(thresh=len(model_results)*0.6,axis=1)\n",
    "    model_results = model_results.dropna()\n",
    "    print(model_results.shape)\n",
    "    modelnames  = model_results.columns.to_list()[2:] \n",
    "    for col in model_results[modelnames].select_dtypes(include=['object']).columns:\n",
    "        model_results[col] = model_results[col].astype(np.float32)\n",
    "    # 计算每个预测值与真实值之间的偏差率\n",
    "    for model in modelnames:\n",
    "        model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['y']\n",
    "\n",
    "    # 获取每行对应的最小偏差率值\n",
    "    min_abs_error_rate_values = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)\n",
    "    # 获取每行对应的最小偏差率值对应的列名\n",
    "    min_abs_error_rate_column_name = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)\n",
    "    print(min_abs_error_rate_column_name)\n",
    "    # 将列名索引转换为列名\n",
    "    min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])\n",
    "    # 取出现次数最多的模型名称\n",
    "    most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()\n",
    "    logger.info(f\"最近20天预测残差最低的模型名称：{most_common_model}\")\n",
    "\n",
    "    # 保存结果到数据库\n",
    "    \n",
    "    if not sqlitedb.check_table_exists('most_model'):\n",
    "        sqlitedb.create_table('most_model',columns=\"ds datetime, most_common_model TEXT\")\n",
    "    sqlitedb.insert_data('most_model',(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),most_common_model,),columns=('ds','most_common_model',))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ade7026e-8cf2-405f-a2da-9e90f364adab",
   "metadata": {},
   "outputs": [],
   "source": [
    "if is_corr:\n",
    "    df = corr_feature(df=df)\n",
    "\n",
    "df1 = df.copy()  # 备份一下，后面特征筛选完之后加入ds y 列用\n",
    "logger.info(f\"开始训练模型...\")\n",
    "row,col = df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfef57d8-36da-423b-bbe7-05a13e15f71b",
   "metadata": {},
   "outputs": [],
   "source": [
    "now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')\n",
    "ex_Model(df,\n",
    "        horizon=horizon,\n",
    "        input_size=input_size,\n",
    "        train_steps=train_steps,\n",
    "        val_check_steps=val_check_steps,\n",
    "        early_stop_patience_steps=early_stop_patience_steps,\n",
    "        is_debug=is_debug,\n",
    "        dataset=dataset,\n",
    "        is_train=is_train,\n",
    "        is_fivemodels=is_fivemodels,\n",
    "        val_size=val_size,\n",
    "        test_size=test_size,\n",
    "        settings=settings,\n",
    "        now=now,\n",
    "        etadata = etadata,\n",
    "        modelsindex = modelsindex,\n",
    "        data = data,\n",
    "        is_eta=is_eta,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e5b6f30-b7ca-4718-97a3-48b54156e07f",
   "metadata": {},
   "outputs": [],
   "source": [
    "logger.info('模型训练完成')\n",
    "# # 模型评估\n",
    "\n",
    "pd.set_option('display.max_columns', 100)\n",
    "# 计算预测评估指数\n",
    "def model_losss_juxiting(sqlitedb):\n",
    "    global dataset\n",
    "    # 数据库查询最佳模型名称\n",
    "    most_model = [sqlitedb.select_data('most_model',columns=['most_common_model'],order_by='ds desc',limit=1).values[0][0]]\n",
    "    most_model_name = most_model[0]\n",
    "\n",
    "    # 预测数据处理 predict\n",
    "    df_combined = loadcsv(os.path.join(dataset,\"cross_validation.csv\"))  \n",
    "    df_combined = dateConvert(df_combined)\n",
    "    # 删除空列\n",
    "    df_combined.dropna(axis=1,inplace=True)\n",
    "     # 删除缺失值,预测过程不能有缺失值\n",
    "    df_combined.dropna(inplace=True) \n",
    "    # 其他列转为数值类型\n",
    "    df_combined = df_combined.astype({col: 'float32' for col in df_combined.columns if col not in ['cutoff','ds'] })\n",
    "    # 使用 groupby 和 transform 结合 lambda 函数来获取每个分组中 cutoff 的最小值，并创建一个新的列来存储这个最大值\n",
    "    df_combined['max_cutoff'] = df_combined.groupby('ds')['cutoff'].transform('max')\n",
    "\n",
    "    # 然后筛选出那些 cutoff 等于 max_cutoff 的行，这样就得到了每个分组中 cutoff 最大的行，并保留了其他列\n",
    "    df_combined = df_combined[df_combined['cutoff'] == df_combined['max_cutoff']]\n",
    "    # 删除模型生成的cutoff列\n",
    "    df_combined.drop(columns=['cutoff', 'max_cutoff'], inplace=True)\n",
    "    # 获取模型名称\n",
    "    modelnames  = df_combined.columns.to_list()[1:] \n",
    "    if 'y' in modelnames:\n",
    "        modelnames.remove('y')\n",
    "    df_combined3 = df_combined.copy()  # 备份df_combined,后面画图需要\n",
    "\n",
    "\n",
    "    # 空的列表存储每个模型的MSE、RMSE、MAE、MAPE、SMAPE\n",
    "    cellText = []\n",
    "\n",
    "    # 遍历模型名称，计算模型评估指标  \n",
    "    for model in modelnames:\n",
    "        modelmse = mse(df_combined['y'], df_combined[model])\n",
    "        modelrmse = rmse(df_combined['y'], df_combined[model])\n",
    "        modelmae = mae(df_combined['y'], df_combined[model])\n",
    "        # modelmape = mape(df_combined['y'], df_combined[model])\n",
    "        # modelsmape = smape(df_combined['y'], df_combined[model])\n",
    "        # modelr2 = r2_score(df_combined['y'], df_combined[model])\n",
    "        cellText.append([model,round(modelmse, 3), round(modelrmse, 3), round(modelmae, 3)])\n",
    "        \n",
    "    model_results3 = pd.DataFrame(cellText,columns=['模型(Model)','平均平方误差(MSE)', '均方根误差(RMSE)',  '平均绝对误差(MAE)'])\n",
    "    # 按MSE降序排列\n",
    "    model_results3 = model_results3.sort_values(by='平均平方误差(MSE)', ascending=True)\n",
    "    model_results3.to_csv(os.path.join(dataset,\"model_evaluation.csv\"),index=False)\n",
    "    modelnames = model_results3['模型(Model)'].tolist()\n",
    "    allmodelnames = modelnames.copy()\n",
    "    # 保存5个最佳模型的名称\n",
    "    if len(modelnames) > 5:\n",
    "        modelnames = modelnames[0:5]\n",
    "    with open(os.path.join(dataset,\"best_modelnames.txt\"), 'w') as f:\n",
    "        f.write(','.join(modelnames) + '\\n')\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",
    "    best_models = pd.read_csv(os.path.join(dataset,'best_modelnames.txt'),header=None).values.flatten().tolist()\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",
    "    # 设置有5个子图的画布\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",
    "    # 历史数据+预测数据\n",
    "    # 拼接未来时间预测\n",
    "    df_predict  = loadcsv(os.path.join(dataset,'predict.csv'))\n",
    "    df_predict.drop('unique_id',inplace=True,axis=1)\n",
    "    df_predict.dropna(axis=1,inplace=True)\n",
    "    df_predict2 = df_predict.copy()\n",
    "    try:\n",
    "        df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y-%m-%d')\n",
    "    except ValueError :\n",
    "        df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y/%m/%d')\n",
    "\n",
    "    # 取第一行数据存储到数据库中\n",
    "    first_row = df_predict.head(1)\n",
    "    first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00')\n",
    "\n",
    "    # # 将预测结果保存到数据库\n",
    "    # # 判断表存在\n",
    "    if not sqlitedb.check_table_exists('testandpredict_groupby'):\n",
    "        df_predict2.to_sql('testandpredict_groupby',sqlitedb.connection,index=False)\n",
    "    else:\n",
    "        for row in df_predict2.itertuples(index=False):\n",
    "            row_dict = row._asdict()\n",
    "            check_query = sqlitedb.select_data('testandpredict_groupby',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('testandpredict_groupby',set_clause,where_condition = f\"ds = '{row.ds}'\")\n",
    "                continue\n",
    "            sqlitedb.insert_data('testandpredict_groupby',tuple(row_dict.values()),columns=row_dict.keys())\n",
    "\n",
    "    df_combined3 = pd.concat([df_combined3, df_predict]).reset_index(drop=True)\n",
    "\n",
    "    # # 判断 df 的数值列转为float\n",
    "    for col in df_combined3.columns:\n",
    "        try:\n",
    "            if col != 'ds':\n",
    "                df_combined3[col] = df_combined3[col].astype(float)\n",
    "                df_combined3[col] = df_combined3[col].round(2)\n",
    "        except ValueError:\n",
    "            pass\n",
    "    df_combined3.to_csv(os.path.join(dataset,\"df_combined3.csv\"),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",
    "    ten_models = allmodelnames\n",
    "    # 计算每个模型的方差\n",
    "    variances = df_combined3[ten_models].var()\n",
    "    # 找到方差最大的模型\n",
    "    max_variance_model = variances.idxmax()\n",
    "    # 打印方差最大的模型\n",
    "    print(\"方差最大的模型是：\", max_variance_model)\n",
    "    # 去掉方差最大的模型\n",
    "    df_combined3 = df_combined3.drop(columns=[max_variance_model])\n",
    "    if max_variance_model in allmodelnames:\n",
    "        allmodelnames.remove(max_variance_model)\n",
    "    df_combined3['min'] = df_combined3[allmodelnames].min(axis=1)\n",
    "    df_combined3['max'] = df_combined3[allmodelnames].max(axis=1)\n",
    "    print(df_combined3[['min','max']])\n",
    "    # 历史价格+预测价格\n",
    "    df_combined3 = df_combined3[-50:] # 取50个数据点画图\n",
    "    plt.figure(figsize=(20, 10))\n",
    "    plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值',marker='o')\n",
    "    plt.plot(df_combined3['ds'], df_combined3[most_model], label=most_model_name)\n",
    "    plt.fill_between(df_combined3['ds'], df_combined3['min'], df_combined3['max'], alpha=0.2)\n",
    "    plt.grid(True)\n",
    "    # 当前日期画竖虚线\n",
    "    plt.axvline(x=df_combined3['ds'].iloc[-horizon], color='r', linestyle='--')\n",
    "    plt.legend()\n",
    "    plt.xlabel('日期')\n",
    "    plt.ylabel('价格')\n",
    "\n",
    "    # # 显示历史值\n",
    "    for i, j in zip(df_combined3['ds'][:-5], df_combined3['y'][:-5]):\n",
    "        plt.text(i, j, str(j), ha='center', va='bottom')\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",
    "\n",
    "\n",
    "       \n",
    "    # 预测值表格\n",
    "    fig, ax = plt.subplots(figsize=(20, 6))\n",
    "    ax.axis('off')  # 关闭坐标轴\n",
    "    # 数值保留2位小数\n",
    "    df_combined3 = df_combined3.round(2)\n",
    "    df_combined3 = df_combined3[-horizon:]\n",
    "    df_combined3['Day'] = [f'Day_{i}' for i in range(1,horizon+1)]\n",
    "    # Day列放到最前面\n",
    "    df_combined3 = df_combined3[['Day'] + list(df_combined3.columns[:-1])]\n",
    "    table = ax.table(cellText=df_combined3.values, colLabels=df_combined3.columns, loc='center')\n",
    "    #加宽表格\n",
    "    table.auto_set_font_size(False)\n",
    "    table.set_fontsize(10)\n",
    "\n",
    "    # 设置表格样式，列数据最小的用绿色标识\n",
    "    plt.savefig(os.path.join(dataset,'预测值表格.png'), bbox_inches='tight')\n",
    "    plt.close()\n",
    "    # plt.show()\n",
    "       \n",
    "    # 可视化评估结果\n",
    "    plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "    fig, ax = plt.subplots(figsize=(20, 10))\n",
    "    ax.axis('off')  # 关闭坐标轴\n",
    "    table = ax.table(cellText=model_results3.values, colLabels=model_results3.columns, loc='center')\n",
    "    # 加宽表格\n",
    "    table.auto_set_font_size(False)\n",
    "    table.set_fontsize(10)\n",
    "\n",
    "    # 设置表格样式，列数据最小的用绿色标识\n",
    "    plt.savefig(os.path.join(dataset,'模型评估.png'), bbox_inches='tight')\n",
    "    plt.close()\n",
    "    return model_results3\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "logger.info('训练数据绘图ing')\n",
    "model_results3 = model_losss_juxiting(sqlitedb)\n",
    "\n",
    "logger.info('训练数据绘图end')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85b557de-8235-4e27-b5b8-58b36dfe6724",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型报告\n",
    "\n",
    "logger.info('制作报告ing')\n",
    "title = f'{settings}--{now}-预测报告' # 报告标题\n",
    "\n",
    "pp_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,\n",
    "        reportname=reportname,sqlitedb=sqlitedb),\n",
    "\n",
    "logger.info('制作报告end')\n",
    "logger.info('模型训练完成')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4129e71-ee2c-4af1-81ed-fadf14efa206",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 发送邮件\n",
    "m = SendMail(\n",
    "    username=username,\n",
    "    passwd=passwd,\n",
    "    recv=recv,\n",
    "    title=title,\n",
    "    content=content,\n",
    "    file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),\n",
    "    ssl=ssl,\n",
    ")\n",
    "# m.send_mail()   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fd18381-ca65-4190-b42b-4e602c1138be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce150fc7-2d3b-4599-846e-cfb436515771",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
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--- a/预测值绘图调试.ipynb
+++ b/预测值绘图调试.ipynb
`@ -1 +1 @@`
	`NLinear,RNN,TiDE,LSTM,DilatedRNN`	`NLinear,RNN,TiDE,TSMixer,PatchTST`