diff --git a/codeback.py b/codeback.py new file mode 100644 index 0000000..2b88642 --- /dev/null +++ b/codeback.py @@ -0,0 +1,494 @@ + +################################### 报告内容 ################################## + + # 根据真实值分组,去掉最高最小预测值画图逻辑 + # content.append(Graphs.draw_text('图示说明:')) + # content.append(Graphs.draw_text('1. 将所有模型的预测结果进行分组,大于真实值的为一组,小于真实值的为一组,去掉最高的预测值,去掉最小的预测值')) + # content.append(Graphs.draw_text('2. 确定通道上界:在大于真实值的分组中,取最大的预测值')) + # content.append(Graphs.draw_text('3. 确定通道下界:在小于真实值的分组中,取第二小的预测值')) + # content.append(Graphs.draw_text('4. 预测结果没有真实值作为参考依据,通道上界取近20个交易日内预测在上界值的模型对应的预测值,通道下界同理;')) + # content.append(Graphs.draw_text('5. 预测结果选用近20个交易日内,最多接近真实值的模型的预测值对应的预测结果;')) + # content.append(Graphs.draw_text('6. 预测结果在通道外的,代表最接近真实值的预测结果不在置信波动范围内。')) + # 波动率画图逻辑 + # content.append(Graphs.draw_text('图示说明:')) + # content.append(Graphs.draw_text('1. 确定波动率置信区间:统计近60个交易日的真实价格波动率,找出在 10% ,90% 的分位值作为波动率置信区间;')) + # content.append(Graphs.draw_text('2. 确定通道上界:在所有模型的预测结果中 <= 前一天真实价格 乘以 90%的置信波动分位数')) + # content.append(Graphs.draw_text('3. 确定通道下界:在所有模型的预测结果中 >= 前一天真实价格 乘以 10%的置信波动分位数')) + # content.append(Graphs.draw_text('4. 预测结果没有真实值作为参考依据,通道上界取近20个交易日内预测在上界值的模型对应的预测值,通道下界同理;')) + # content.append(Graphs.draw_text('5. 预测结果选用近20个交易日内,最多接近真实值的模型的预测值对应的预测结果;')) + # content.append(Graphs.draw_text('6. 预测结果在通道外的,代表最接近真实值的预测结果不在置信波动范围内。')) + + # # 计算特征相关性 + # data.rename(columns={y: 'y'}, inplace=True) + # data['ds'] = pd.to_datetime(data['ds']) + # data.drop(columns=['ds'], inplace=True) + # # 创建一个空的 DataFrame 来保存相关系数 + # correlation_df = pd.DataFrame(columns=['Feature', 'Correlation']) + # # 计算各特征与目标列的皮尔逊相关系数,并保存到新的 Data 中 + # for col in data.columns: + # if col!= 'y': + # pearson_correlation = np.corrcoef(data[col], data['y'])[0, 1] + # spearman_correlation, _ = spearmanr(data[col], data['y']) + # new_row = {'Feature': col, 'Pearson_Correlation': round(pearson_correlation,3), 'Spearman_Correlation': round(spearman_correlation,2)} + # correlation_df = correlation_df._append(new_row, ignore_index=True) + + # correlation_df.drop('Correlation', axis=1, inplace=True) + # correlation_df.dropna(inplace=True) + # correlation_df.to_csv(os.path.join(dataset,'指标相关性分析.csv'), index=False) + + # data = correlation_df['Pearson_Correlation'].values.tolist() + # # 生成 -1 到 1 的 20 个区间 + # bins = np.linspace(-1, 1, 21) + # # 计算每个区间的统计数(这里是区间内数据的数量) + # hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)] + + # #设置画布大小 + # plt.figure(figsize=(10, 6)) + # # 绘制直方图 + # plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0])) + + # # 添加标题和坐标轴标签 + # plt.title('皮尔逊相关系数分布图') + # plt.xlabel('区间') + # plt.ylabel('统计数') + # plt.savefig(os.path.join(dataset, '皮尔逊相关性系数.png')) + # plt.close() + + + # #设置画布大小 + # plt.figure(figsize=(10, 6)) + # data = correlation_df['Spearman_Correlation'].values.tolist() + # # 计算每个区间的统计数(这里是区间内数据的数量) + # hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)] + + # # 绘制直方图 + # plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0])) + + # # 添加标题和坐标轴标签 + # plt.title('斯皮尔曼相关系数分布图') + # plt.xlabel('区间') + # plt.ylabel('统计数') + # plt.savefig(os.path.join(dataset, '斯皮尔曼相关性系数.png')) + # plt.close() + # content.append(Graphs.draw_text(f'指标相关性分析--皮尔逊相关系数:')) + # # 皮尔逊正相关 不相关 负相关 的表格 + # content.append(Graphs.draw_img(os.path.join(dataset,'皮尔逊相关性系数.png'))) + # content.append(Graphs.draw_text('''皮尔逊相关系数说明:''')) + # content.append(Graphs.draw_text('''衡量两个特征之间的线性相关性。''')) + # content.append(Graphs.draw_text(''' + # 相关系数为1:表示两个变量之间存在完全正向的线性关系,即当一个变量增加时,另一个变量也相应增加,且变化是完全一致的。''')) + # content.append(Graphs.draw_text('''当前特征中正相关前十的有:''')) + # top10_columns = correlation_df.sort_values(by='Pearson_Correlation',ascending=False).head(10)['Feature'].to_list() + # top10 = ','.join(top10_columns) + # content.append(Graphs.draw_text(f'''{top10}''')) + + # feature_df = feature_data_df[['ds','y']+top10_columns] + # # 遍历X每一列,和yy画散点图 , + # for i, col in enumerate(feature_df.columns): + # print(f'正在绘制第{i+1}个特征{col}与价格散点图...') + # if col not in ['ds', 'y']: + # fig, ax1 = plt.subplots(figsize=(10, 6)) + # # 在第一个坐标轴上绘制数据 + # ax1.plot(feature_df['ds'], feature_df['y'], 'b-') + # ax1.set_xlabel('日期') + # ax1.set_ylabel('y', color='b') + # ax1.tick_params('y', colors='b') + # # 在 ax1 上添加文本显示值,添加一定的偏移避免值与曲线重叠 + # for j in range(1,len(feature_df),2): + # value = feature_df['y'].iloc[j] + # date = feature_df['ds'].iloc[j] + # offset = 1.001 + # ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10) + # # 创建第二个坐标轴 + # ax2 = ax1.twinx() + # # 在第二个坐标轴上绘制数据 + # line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-') + # ax2.set_ylabel(col, color='r') + # ax2.tick_params('y', colors='r') + # # 在 ax2 上添加文本显示值,添加一定的偏移避免值与曲线重叠 + # for j in range(0,len(feature_df),2): + # value = feature_df[col].iloc[j] + # date = feature_df['ds'].iloc[j] + # offset = 1.001 + # ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10) + # # 添加标题 + # plt.title(col) + # # 设置横坐标为日期格式并自动调整 + # locator = mdates.AutoDateLocator() + # formatter = mdates.AutoDateFormatter(locator) + # ax1.xaxis.set_major_locator(locator) + # ax1.xaxis.set_major_formatter(formatter) + # # 文件名特殊字符处理 + # col = col.replace('*', '-') + # col = col.replace(':', '-') + # plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png')) + # content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png'))) + # plt.close() + + + # content.append(Graphs.draw_text(f'指标相关性分析--斯皮尔曼相关系数:')) + # # 皮尔逊正相关 不相关 负相关 的表格 + # content.append(Graphs.draw_img(os.path.join(dataset,'斯皮尔曼相关性系数.png'))) + # content.append(Graphs.draw_text('斯皮尔曼相关系数(Spearmans rank correlation coefficient)是一种用于衡量两个变量之间的单调关系(不一定是线性关系)的统计指标。')) + # content.append(Graphs.draw_text('它的计算基于变量的秩次(即变量值的排序位置)而非变量的原始值。')) + # content.append(Graphs.draw_text('斯皮尔曼相关系数的取值范围在 -1 到 1 之间。')) + # content.append(Graphs.draw_text('当系数为 1 时,表示两个变量之间存在完全正的单调关系;')) + # content.append(Graphs.draw_text('''当前特征中正单调关系前十的有:''')) + # top10_columns = correlation_df.sort_values(by='Spearman_Correlation',ascending=False).head(10)['Feature'].to_list() + # top10 = ','.join(top10_columns) + # content.append(Graphs.draw_text(f'''{top10}''')) + + # feature_df = feature_data_df[['ds','y']+top10_columns] + # # 遍历X每一列,和yy画散点图 , + # for i, col in enumerate(feature_df.columns): + # print(f'正在绘制第{i+1}个特征{col}与价格散点图...') + # if col not in ['ds', 'y']: + # fig, ax1 = plt.subplots(figsize=(10, 6)) + # # 在第一个坐标轴上绘制数据 + # ax1.plot(feature_df['ds'], feature_df['y'], 'b-') + # ax1.set_xlabel('日期') + # ax1.set_ylabel('y', color='b') + # ax1.tick_params('y', colors='b') + # # 在 ax1 上添加文本显示值,添加一定的偏移避免值与曲线重叠 + # for j in range(1,len(feature_df),2): + # value = feature_df['y'].iloc[j] + # date = feature_df['ds'].iloc[j] + # offset = 1.001 + # ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10) + # # 创建第二个坐标轴 + # ax2 = ax1.twinx() + # # 在第二个坐标轴上绘制数据 + # line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-') + # ax2.set_ylabel(col, color='r') + # ax2.tick_params('y', colors='r') + # # 在 ax2 上添加文本显示值,添加一定的偏移避免值与曲线重叠 + # for j in range(0,len(feature_df),2): + # value = feature_df[col].iloc[j] + # date = feature_df['ds'].iloc[j] + # offset = 1.001 + # ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10) + # # 添加标题 + # plt.title(col) + # # 设置横坐标为日期格式并自动调整 + # locator = mdates.AutoDateLocator() + # formatter = mdates.AutoDateFormatter(locator) + # ax1.xaxis.set_major_locator(locator) + # ax1.xaxis.set_major_formatter(formatter) + # # 文件名特殊字符处理 + # col = col.replace('*', '-') + # col = col.replace(':', '-') + # plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png')) + # content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png'))) + # plt.close() + + # content.append(Graphs.draw_text('当系数为 -1 时,表示存在完全负的单调关系;')) + # content.append(Graphs.draw_text('''当前特征中负单调关系前十的有:''')) + # tail10_columns = correlation_df.sort_values(by='Spearman_Correlation',ascending=True).head(10)['Feature'].to_list() + # top10 = ','.join(tail10_columns) + # content.append(Graphs.draw_text(f'''{top10}''')) + # # 获取特征的近一周值 + # feature_df = feature_data_df[['ds','y']+tail10_columns] + # # 遍历X每一列,和yy画散点图 , + # for i, col in enumerate(feature_df.columns): + # print(f'正在绘制第{i+1}个特征{col}与价格散点图...') + # if col not in ['ds', 'y']: + # fig, ax1 = plt.subplots(figsize=(10, 6)) + # # 在第一个坐标轴上绘制数据 + # ax1.plot(feature_df['ds'], feature_df['y'], 'b-') + # ax1.set_xlabel('日期') + # ax1.set_ylabel('y', color='b') + # ax1.tick_params('y', colors='b') + # # 在 ax1 上添加文本显示值,添加一定的偏移避免值与曲线重叠 + # for j in range(len(feature_df)): + # if j%2 == 1: + # value = feature_df['y'].iloc[j] + # date = feature_df['ds'].iloc[j] + # offset = 1.001 + # ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10) + # # 创建第二个坐标轴 + # ax2 = ax1.twinx() + # # 在第二个坐标轴上绘制数据 + # line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-') + # ax2.set_ylabel(col, color='r') + # ax2.tick_params('y', colors='r') + # # 在 ax2 上添加文本显示值,添加一定的偏移避免值与曲线重叠 + # for j in range(1,len(feature_df),2): + # value = feature_df[col].iloc[j] + # date = feature_df['ds'].iloc[j] + # offset = 1.001 + # ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10) + # # 添加标题 + # plt.title(col) + # # 设置横坐标为日期格式并自动调整 + # locator = mdates.AutoDateLocator() + # formatter = mdates.AutoDateFormatter(locator) + # ax1.xaxis.set_major_locator(locator) + # ax1.xaxis.set_major_formatter(formatter) + # # 文件名特殊字符处理 + # col = col.replace('*', '-') + # col = col.replace(':', '-') + # plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png')) + # content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png'))) + # plt.close() + # content.append(Graphs.draw_text('当系数为 0 时,表示两个变量之间不存在单调关系。')) + # content.append(Graphs.draw_text('与皮尔逊相关系数相比,斯皮尔曼相关系数对于数据中的异常值不敏感,更适用于处理非线性关系或存在极端值的数据。')) + + + + + + +################################### 预测值与真实值绘图逻辑 ################################## + # # 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 + # def find_min_max_within_quantile(row): + # true_value = row['y'] + # row.drop(['ds','y'], inplace=True) + # row = row.astype(float).round(2) + + # max_heap = [] + # min_heap = [] + # for col in row.index: + # # 对比真实值进行分类 + # if row[col] < true_value: + # heapq.heappush(min_heap, row[col]) + # elif row[col] > true_value: + # heapq.heappush(max_heap, -row[col]) # 使用负号来实现最大堆 + + # if len(max_heap) == 1: + # max_y = max_heap[0] + # elif len(max_heap) == 0: + # max_y = -min_heap[-1] + # else: + # max_y = heapq.nsmallest(2, max_heap)[1] + + # if len(min_heap) < 2 : + # min_y = -max_heap[-1] + # else: + # min_y = heapq.nsmallest(2, min_heap)[-1] + + + # # 获取最大和最小的值 + # q10 = min_y + # q90 = -max_y + + # # 获取最大和最小的模型名称 + # min_model = row[row == q10].idxmin() + # max_model = row[row == q90].idxmax() + + # # 设置上下界比例 + # rote = 1 + + # q10 = q10 * rote + # q90 = q90 * rote + + # logger.info(min_model,q10,max_model,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) + + #使用最佳五个模型进行绘图 + # 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) + + # # 通道使用模型评估前80%作为置信度 + # def find_min_max_within_quantile(row): + # row.drop(['ds','y'], inplace=True) + # row = row.astype(float).round(2) + + # row_sorted = row + # # 计算 10% 和 90% 位置的索引 + # index_10 = 0 + # index_90 = int(len(row_sorted) * 0.8) + # q10 = row_sorted[index_10] + # q90 = row_sorted[index_90] + # # 获取模型名称 + # 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']) + + # # 重新排列 + # df_combined3 = df_combined3[['ds','y'] + allmodelnames] + # # 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) + + + # # 通道使用预测模型的80%置信度 + # def find_min_max_within_quantile(row): + # row.drop(['ds','y'], inplace=True) + # row = row.astype(float).round(2) + + # row_sorted = row.sort_values(ascending=True).reset_index(drop=True) + # # 计算 10% 和 90% 位置的索引 + # index_10 = int(len(row_sorted) * 0.1) + # index_90 = int(len(row_sorted) * 0.9) + # q10 = row_sorted[index_10] + # q90 = row_sorted[index_90] + # # 获取模型名称 + # 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) + + + + + # # 计算波动率 + # df_combined3['volatility'] = df_combined3['y'].pct_change().round(4) + # # 计算近60日的波动率 10% 90%分位数 + # df_combined3['quantile_10'] = df_combined3['volatility'].rolling(60).quantile(0.1) + # df_combined3['quantile_90'] = df_combined3['volatility'].rolling(60).quantile(0.9) + # df_combined3 = df_combined3.round(4) + # # 计算分位数对应的价格 + # df_combined3['quantile_10_price'] = df_combined3['y'] * (1 + df_combined3['quantile_10']) + # df_combined3['quantile_90_price'] = df_combined3['y'] * (1 + df_combined3['quantile_90']) + + # # 遍历行 + # def find_min_max_within_quantile(row): + # # 获取分位数10%和90%的值 + # q10 = row['quantile_10_price'] + # q90 = row['quantile_90_price'] + + # # 判断flot值是否为空值 + # if pd.isna(q10) or pd.isna(q90): + # return pd.Series([None, None, None, None], index=['min_within_quantile','max_within_quantile','min_model','max_model']) + + # # 初始化最小和最大值为None + # min_value = None + # max_value = None + # min_value_model = '' + # max_value_model = '' + + + # # 遍历指定列,找出在分位数范围内的最大最小值 + # for model in modelnames: + # value = row[model] + # if value >= q10 and value <= q90: + # if min_value is None or value < min_value: + # min_value = value + # min_value_model = model + + # if max_value is None or value > max_value: + # max_value = value + # max_value_model = model + + # 返回最大最小值 + # return pd.Series([min_value, max_value,min_value_model,max_value_model], index=['min_within_quantile', 'max_within_quantile','min_model','max_model']) + +# # # 应用函数到每一行 +# 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) +# # # 保存到数据库 +# 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) + + +# # 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 + + +# # 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) +# df_predict2 = df_predict.copy() +# 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 +# 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,"testandpredict_groupby.csv"),index=False) +# df_combined3['ds'] = df_combined3['ds'].dt.strftime('%Y-%m-%d 00:00:00') +# # # 判断表存在 +# if not sqlitedb.check_table_exists('testandpredict_groupby'): +# df_combined3.to_sql('testandpredict_groupby',sqlitedb.connection,index=False) +# else: +# 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: +# 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()) + +# ten_models = allmodelnames +# # 计算每个模型的方差 +# variances = df_combined3[ten_models].var() +# # 找到方差最大的模型 +# max_variance_model = variances.idxmax() +# # 打印方差最大的模型 +# print("方差最大的模型是:", max_variance_model) +# # 去掉方差最大的模型 +# df_combined3 = df_combined3.drop(columns=[max_variance_model]) +# if max_variance_model in allmodelnames: +# allmodelnames.remove(max_variance_model) +# df_combined3['min'] = df_combined3[allmodelnames].min(axis=1) +# df_combined3['max'] = df_combined3[allmodelnames].max(axis=1) +# print(df_combined3[['min','max']]) +# # 历史价格+预测价格 +# df_combined3 = df_combined3[-50:] # 取50个数据点画图 +# plt.figure(figsize=(20, 10)) +# plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值',marker='o') +# plt.plot(df_combined3['ds'], df_combined3[most_model], label=most_model_name) +# plt.fill_between(df_combined3['ds'], df_combined3['min'], df_combined3['max'], alpha=0.2) +# plt.grid(True) +# # # 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') +# # 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() \ No newline at end of file diff --git a/config_juxiting.py b/config_juxiting.py index 79c29d1..4e00968 100644 --- a/config_juxiting.py +++ b/config_juxiting.py @@ -249,7 +249,8 @@ val_size = test_size # 验证集大小,同测试集大小 ### 特征筛选用到的参数 k = 100 # 特征筛选数量,如果是0或者值比特征数量大,代表全部特征 - +# 绘图预测图上下边界使用的阈值,可以根据实际情况调整 +rote = 0.04 ### 文件 data_set = 'PP指标数据.xlsx' # 数据集文件 diff --git a/juxitingdataset/PP指标数据.xlsx b/juxitingdataset/PP指标数据.xlsx index fac7701..f797679 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/cross_validation.csv b/juxitingdataset/cross_validation.csv index 16e0457..5efd49c 100644 --- a/juxitingdataset/cross_validation.csv +++ b/juxitingdataset/cross_validation.csv @@ -1,981 +1,981 @@ ds,cutoff,NHITS,Informer,LSTM,iTransformer,TSMixer,TSMixerx,PatchTST,RNN,GRU,TCN,BiTCN,DilatedRNN,MLP,DLinear,NLinear,TFT,FEDformer,StemGNN,MLPMultivariate,TiDE,DeepNPTS,y -2024-01-30,2024-01-29,7314.251,7335.7573,7255.3174,7375.6104,7296.976,7304.239,7288.5317,7316.801,7357.7744,7406.3555,7330.3247,7284.3076,7336.1597,7282.5674,7305.315,7289.839,,7306.1157,7295.5366,7297.1167,7304.877,7315.0 -2024-01-31,2024-01-29,7298.995,7335.829,7265.338,7405.045,7299.156,7318.3486,7281.5654,7306.621,7372.8306,7423.019,7323.468,7322.599,7343.584,7298.521,7314.255,7284.034,,7303.2783,7293.6396,7297.248,7304.483,7302.5 -2024-02-01,2024-01-29,7305.7793,7340.6836,7277.1284,7436.173,7300.5234,7297.923,7280.0728,7287.096,7363.8066,7448.726,7329.108,7343.4175,7353.556,7276.5396,7316.443,7280.888,,7294.403,7337.911,7303.125,7303.1465,7275.0 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-2024-02-07,2024-02-04,7234.7065,7299.937,7234.1055,7176.702,7210.578,7188.1094,7196.2075,7237.733,7317.4795,7426.8354,7219.72,7274.9,7223.246,7231.1055,7226.5044,7216.7197,,7280.6636,7175.688,7199.57,7275.6895,7255.0 -2024-02-08,2024-02-04,7232.3438,7283.8345,7250.9673,7183.939,7205.344,7191.949,7175.9346,7262.566,7340.5723,7425.8,7208.0625,7336.7456,7211.7554,7250.0615,7242.409,7213.4414,,7299.969,7182.6113,7216.5425,7240.312,7270.0 -2024-02-18,2024-02-04,7222.976,7294.122,7281.2065,7187.585,7198.442,7189.1777,7158.727,7306.5938,7352.742,7407.16,7217.132,7386.3774,7210.9346,7255.8687,7251.7954,7209.6206,,7291.2188,7158.541,7214.9526,7275.0435,7336.666666666667 -2024-02-06,2024-02-05,7224.19,7245.7725,7203.255,7200.7515,7215.0327,7174.849,7206.815,7257.774,7300.4937,7369.699,7214.125,7210.8745,7217.4287,7211.9985,7214.206,7214.9854,,7288.057,7159.4814,7215.9033,7300.921,7246.666666666667 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+2024-11-19,2024-11-12,7307.739,7292.028,7310.3115,7204.046,7332.0044,7325.749,7319.125,7335.4976,7371.578,7436.033,7333.476,7305.7773,7369.1274,7344.596,7332.8574,7272.761,,7323.6455,7325.4717,7334.741,7399.9863,7370.0 diff --git a/juxitingdataset/df_combined3.csv b/juxitingdataset/df_combined3.csv index 80d7ed8..9c50c6a 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,NLinear-NHITS-误差比例,RNN-NHITS-误差比例,TiDE-NHITS-误差比例,TSMixer-NHITS-误差比例,PatchTST-NHITS-误差比例,NHITS-NHITS-误差比例,DilatedRNN-NHITS-误差比例,DLinear-NHITS-误差比例,GRU-NHITS-误差比例,LSTM-NHITS-误差比例,Informer-NHITS-误差比例,DeepNPTS-NHITS-误差比例,MLP-NHITS-误差比例,TFT-NHITS-误差比例,TSMixerx-NHITS-误差比例,BiTCN-NHITS-误差比例,TCN-NHITS-误差比例,iTransformer-NHITS-误差比例,StemGNN-NHITS-误差比例,MLPMultivariate-NHITS-误差比例,upper_bound,lower_bound,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 -2024-01-30,7314.25,7335.76,7255.32,7375.61,7296.98,7304.24,7288.53,7316.8,7357.77,7406.36,7330.32,7284.31,7336.16,7282.57,7305.31,7289.84,7306.12,7295.54,7297.12,7304.88,7315.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,7255.32,7406.36,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,7314.25,NHITS -2024-01-31,7313.85,7333.85,7260.27,7376.32,7306.93,7303.91,7291.97,7327.11,7364.25,7409.82,7340.18,7298.74,7321.85,7294.2,7298.47,7302.1,7304.55,7299.61,7302.49,7313.87,7302.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,7260.27,7409.82,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,7302.49,TiDE -2024-02-01,7297.0,7335.21,7250.98,7385.46,7298.8,7304.56,7284.13,7314.06,7351.66,7406.99,7334.74,7290.45,7319.52,7291.02,7287.84,7290.86,7303.42,7300.2,7301.71,7297.39,7275.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.02,0.01,0.0,0.0,7250.98,7406.99,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.02,0.02,0.0,0.0,7284.13,PatchTST -2024-02-02,7275.51,7314.77,7233.06,7349.83,7272.41,7282.94,7263.86,7294.73,7330.68,7395.3,7306.72,7260.65,7289.64,7278.71,7262.57,7266.32,7300.92,7272.72,7280.73,7271.37,7242.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.0,0.0,7233.06,7395.3,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.02,0.01,0.01,0.0,7233.06,LSTM -2024-02-04,7236.79,7273.36,7212.67,7264.81,7239.09,7232.82,7228.47,7267.82,7308.6,7378.92,7271.96,7228.76,7248.22,7249.75,7245.09,7227.47,7297.04,7210.26,7249.74,7287.39,7240.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.02,0.0,0.01,0.0,7210.26,7378.92,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.02,0.0,0.01,0.0,7239.09,TSMixer -2024-02-05,7237.9,7254.36,7211.69,7215.08,7225.67,7197.03,7226.16,7266.29,7310.45,7374.66,7241.34,7223.16,7238.12,7228.83,7231.89,7224.25,7292.84,7185.72,7230.67,7307.89,7227.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.02,0.0,0.01,0.01,7185.72,7374.66,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.02,0.0,0.01,0.01,7228.83,DLinear -2024-02-06,7224.19,7245.77,7203.25,7200.75,7215.03,7174.85,7206.81,7257.77,7300.49,7369.7,7214.12,7210.87,7217.43,7212.0,7214.21,7214.99,7288.06,7159.48,7215.9,7300.92,7246.67,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.02,0.0,0.01,0.01,7159.48,7369.7,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.02,0.01,0.01,0.01,7245.77,Informer -2024-02-07,7237.08,7241.63,7216.83,7221.2,7226.42,7222.58,7234.44,7277.5,7317.63,7377.89,7198.22,7229.34,7228.86,7227.31,7229.82,7221.54,7284.15,7172.75,7230.4,7282.88,7255.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.02,0.0,0.01,0.01,7172.75,7377.89,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.02,0.0,0.0,0.01,7241.63,Informer -2024-02-08,7249.03,7225.22,7221.61,7236.65,7242.4,7235.89,7252.49,7285.7,7321.4,7382.84,7189.21,7239.06,7239.93,7231.63,7251.7,7236.11,7280.63,7193.89,7252.08,7254.66,7270.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.02,0.0,0.0,0.01,7189.21,7382.84,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.02,0.0,0.0,0.01,7280.63,StemGNN -2024-02-18,7257.58,7232.66,7232.76,7247.51,7258.08,7252.65,7269.86,7295.25,7334.03,7388.26,7192.33,7253.86,7251.03,7230.31,7268.39,7263.02,7278.08,7215.18,7272.44,7244.53,7336.67,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.02,0.0,0.0,0.01,7192.33,7388.26,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.01,0.01,0.01,0.02,7334.03,GRU -2024-02-19,7329.8,7284.9,7279.87,7271.41,7312.25,7328.12,7333.28,7359.55,7391.84,7412.85,7285.85,7321.05,7295.19,7280.82,7344.06,7349.32,7277.16,7268.04,7323.31,7273.12,7350.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,7268.04,7412.85,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.0,0.01,0.01,0.01,0.01,0.01,7349.32,TFT -2024-02-20,7343.01,7301.36,7287.02,7283.48,7338.11,7398.41,7344.57,7358.8,7393.28,7424.57,7353.08,7341.83,7322.19,7304.78,7354.74,7371.01,7276.7,7309.68,7334.03,7338.48,7335.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.0,7276.7,7424.57,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.0,7334.03,TiDE -2024-02-21,7331.02,7289.8,7277.52,7284.16,7334.13,7417.16,7333.0,7344.13,7379.12,7429.64,7381.22,7344.5,7320.66,7332.96,7337.17,7360.45,7275.86,7320.94,7325.44,7337.84,7297.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.0,7275.86,7429.64,0.01,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.02,0.01,0.02,0.0,0.0,0.0,7289.8,Informer -2024-02-22,7286.55,7266.5,7249.85,7285.34,7304.14,7392.3,7305.8,7314.63,7345.37,7420.07,7373.2,7311.53,7302.57,7318.15,7298.16,7284.19,7273.93,7304.0,7292.32,7342.6,7285.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.0,0.01,0.01,0.02,0.0,0.0,0.0,7249.85,7420.07,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.02,0.0,0.0,0.0,7285.34,iTransformer -2024-02-23,7272.61,7258.89,7241.55,7287.63,7288.24,7365.99,7296.96,7295.77,7340.09,7413.29,7359.23,7296.67,7292.49,7303.51,7281.81,7269.99,7271.49,7289.99,7281.0,7323.98,7306.67,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.02,0.0,0.0,0.0,7241.55,7413.29,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.0,0.0,7303.51,DLinear -2024-02-26,7318.19,7258.93,7254.44,7295.47,7299.03,7362.35,7321.52,7317.68,7358.98,7413.28,7357.37,7301.95,7320.65,7296.1,7301.41,7307.13,7269.8,7306.61,7295.1,7371.11,7292.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,7254.44,7413.28,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.0,0.01,0.01,0.02,0.0,0.0,0.0,7295.1,TiDE -2024-02-27,7324.57,7251.97,7242.58,7290.08,7297.98,7317.99,7297.46,7304.28,7342.81,7408.67,7340.79,7289.78,7327.02,7292.81,7282.36,7282.91,7268.05,7299.63,7286.47,7368.65,7306.67,0.01,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.0,7242.58,7408.67,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,7304.28,RNN -2024-02-28,7352.75,7295.49,7254.83,7276.21,7302.82,7348.5,7323.02,7324.8,7359.94,7413.98,7330.58,7305.92,7341.13,7310.9,7305.61,7301.83,7266.96,7303.46,7303.91,7380.02,7353.33,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.01,7254.83,7413.98,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.01,7352.75,NHITS -2024-02-29,7365.17,7313.22,7288.96,7293.72,7334.31,7378.47,7368.02,7371.75,7401.12,7425.51,7341.83,7336.51,7371.22,7333.33,7360.72,7354.87,7266.78,7327.83,7343.33,7374.52,7325.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,7266.78,7425.51,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.0,7327.83,MLPMultivariate -2024-03-01,7320.5,7306.35,7263.73,7277.35,7322.0,7343.2,7329.76,7327.44,7364.73,7416.31,7322.68,7306.52,7337.22,7315.45,7335.42,7314.74,7266.41,7308.63,7327.13,7294.14,7343.33,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7263.73,7416.31,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7343.2,TSMixerx -2024-03-04,7334.56,7330.43,7283.3,7276.89,7330.63,7344.31,7334.27,7357.22,7391.92,7422.3,7315.67,7328.23,7328.13,7333.05,7339.2,7314.98,7266.39,7306.47,7342.18,7296.81,7330.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7266.39,7422.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7330.43,Informer -2024-03-05,7309.45,7325.04,7267.88,7277.67,7326.97,7335.79,7319.16,7340.15,7370.5,7418.13,7305.37,7318.76,7320.53,7326.05,7321.28,7293.51,7266.15,7303.62,7328.04,7281.27,7310.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,7266.15,7418.13,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,7309.45,NHITS -2024-03-06,7304.78,7318.4,7255.75,7276.86,7305.71,7311.24,7313.83,7311.53,7357.06,7412.45,7301.94,7304.3,7315.76,7309.79,7297.15,7279.6,7265.64,7295.52,7301.35,7288.95,7297.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,7255.75,7412.45,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.0,7297.15,NLinear -2024-03-07,7302.21,7321.25,7246.71,7271.87,7294.17,7309.87,7308.66,7314.28,7347.75,7407.47,7294.43,7290.02,7319.87,7285.87,7297.02,7274.12,7264.97,7290.85,7293.32,7295.53,7290.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,7246.71,7407.47,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.0,7290.02,DilatedRNN -2024-03-08,7292.95,7299.1,7241.47,7246.3,7288.79,7302.01,7299.54,7302.63,7343.0,7403.52,7279.52,7288.72,7290.22,7284.58,7288.43,7271.91,7264.57,7279.09,7290.29,7294.8,7295.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.0,0.0,7241.47,7403.52,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,7294.8,DeepNPTS -2024-03-11,7283.5,7308.73,7245.06,7233.56,7289.62,7272.81,7302.29,7308.69,7348.58,7402.53,7275.18,7290.97,7265.94,7297.8,7293.46,7274.37,7264.62,7276.64,7289.43,7294.81,7285.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.0,0.0,7233.56,7402.53,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.0,0.0,7283.5,NHITS -2024-03-12,7264.78,7291.06,7238.1,7239.95,7277.58,7269.54,7282.67,7303.06,7339.16,7395.51,7277.12,7280.3,7244.14,7296.69,7275.29,7269.44,7265.88,7271.18,7270.28,7285.48,7283.33,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.0,7238.1,7395.51,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.01,0.0,0.0,7282.67,PatchTST -2024-03-13,7256.37,7287.32,7238.44,7251.53,7272.1,7275.87,7280.45,7301.9,7340.35,7398.0,7269.77,7285.73,7238.05,7303.68,7278.15,7276.54,7267.4,7268.86,7272.17,7252.64,7285.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.0,7238.05,7398.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.0,0.0,0.0,7285.73,DilatedRNN -2024-03-14,7269.4,7282.06,7239.51,7265.08,7275.51,7277.1,7283.45,7304.5,7341.46,7397.56,7280.69,7284.09,7251.88,7273.88,7288.42,7290.29,7270.84,7275.91,7281.04,7264.11,7277.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.0,7239.51,7397.56,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.0,7277.1,TSMixerx -2024-03-15,7264.23,7275.59,7234.82,7272.71,7271.08,7262.54,7274.31,7295.47,7335.31,7395.98,7277.6,7279.69,7256.92,7252.44,7269.11,7283.47,7273.29,7276.28,7279.45,7273.04,7353.33,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.0,7234.82,7395.98,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.02,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,7335.31,GRU -2024-03-18,7339.36,7324.94,7292.13,7287.28,7320.05,7302.48,7366.6,7371.87,7407.54,7422.89,7304.87,7348.6,7314.84,7304.7,7353.23,7347.31,7276.14,7311.7,7330.94,7344.93,7387.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.0,7276.14,7422.89,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.02,0.01,7371.87,RNN -2024-03-19,7388.08,7385.8,7320.49,7300.95,7361.99,7336.57,7386.98,7397.01,7427.54,7439.6,7311.0,7378.46,7361.53,7326.46,7392.8,7379.9,7277.91,7348.62,7363.5,7387.24,7435.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,7277.91,7439.6,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.02,0.01,0.01,0.01,0.01,0.01,0.02,0.0,0.02,0.02,0.01,7439.6,TCN -2024-03-20,7446.17,7409.96,7390.64,7318.43,7404.71,7354.35,7446.15,7449.59,7481.41,7465.93,7324.92,7439.02,7414.86,7384.65,7440.51,7410.61,7279.73,7384.11,7415.17,7432.14,7460.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.02,0.0,0.02,0.02,0.01,7279.73,7481.41,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.02,0.0,0.02,0.02,0.01,7465.93,TCN -2024-03-21,7471.48,7410.82,7418.15,7359.09,7436.82,7382.55,7467.97,7485.88,7497.73,7485.05,7342.78,7469.99,7437.01,7433.13,7466.88,7407.96,7282.79,7422.37,7449.82,7435.73,7422.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.02,0.0,0.02,0.03,0.01,7282.79,7497.73,0.01,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.02,0.0,7422.37,MLPMultivariate -2024-03-22,7417.1,7386.94,7359.75,7407.99,7419.15,7377.38,7398.66,7419.29,7445.15,7473.64,7348.41,7429.75,7410.0,7426.43,7427.33,7392.96,7286.4,7414.18,7431.13,7377.32,7402.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.02,0.0,7286.4,7473.64,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.02,0.0,7398.66,PatchTST -2024-03-25,7404.65,7354.68,7337.62,7438.0,7391.76,7372.08,7396.19,7403.69,7432.8,7463.86,7342.24,7408.89,7382.35,7426.01,7402.0,7391.26,7288.26,7395.28,7401.38,7352.68,7372.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.01,0.0,0.02,0.0,7288.26,7463.86,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7372.08,TSMixerx -2024-03-26,7367.22,7322.27,7297.47,7379.26,7362.82,7379.8,7348.91,7365.84,7399.45,7449.14,7347.17,7376.77,7355.65,7392.1,7355.2,7356.75,7289.35,7365.31,7355.82,7332.97,7365.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,7289.35,7449.14,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,7365.31,MLPMultivariate -2024-03-27,7348.99,7314.72,7292.01,7324.37,7348.72,7361.01,7349.25,7354.78,7399.57,7441.3,7353.53,7370.99,7335.98,7369.87,7345.71,7338.27,7290.38,7340.31,7351.88,7327.51,7345.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,7290.38,7441.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,7345.71,NLinear -2024-03-28,7315.95,7301.15,7273.48,7295.8,7330.9,7328.15,7333.3,7346.44,7379.1,7428.09,7353.19,7339.56,7312.39,7352.16,7328.61,7330.9,7291.17,7320.32,7333.7,7319.06,7345.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.02,0.0,0.0,0.0,7273.48,7428.09,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7346.44,RNN -2024-03-29,7330.24,7305.84,7275.81,7295.1,7325.45,7312.22,7328.93,7348.29,7385.13,7421.9,7338.67,7331.57,7296.93,7335.55,7336.98,7327.3,7292.35,7309.14,7332.74,7339.16,7345.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,7275.81,7421.9,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7348.29,RNN -2024-04-01,7338.24,7312.92,7275.35,7297.79,7326.11,7304.88,7330.88,7350.42,7384.03,7416.0,7329.56,7326.53,7302.67,7334.68,7344.62,7326.0,7293.29,7308.76,7335.23,7344.61,7360.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7275.35,7416.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.01,7350.42,RNN -2024-04-02,7346.74,7332.9,7289.21,7307.99,7336.79,7317.73,7346.57,7364.25,7401.18,7422.32,7325.94,7337.44,7312.67,7330.29,7355.13,7331.72,7294.23,7313.7,7342.82,7353.49,7380.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7289.21,7422.32,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,7364.25,RNN -2024-04-03,7362.03,7378.23,7308.14,7335.29,7353.97,7345.56,7371.53,7386.28,7419.33,7428.12,7335.65,7363.3,7336.41,7329.53,7376.12,7363.62,7295.82,7333.87,7357.16,7375.72,7397.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,7295.82,7428.12,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,7386.28,RNN -2024-04-07,7380.46,7399.9,7329.28,7342.79,7377.67,7353.99,7410.47,7402.89,7435.25,7440.61,7349.37,7386.35,7366.33,7351.98,7390.83,7357.89,7298.55,7362.64,7388.62,7390.25,7437.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7298.55,7440.61,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.02,0.01,7435.25,GRU -2024-04-08,7447.65,7446.34,7385.22,7363.92,7413.36,7379.34,7464.31,7450.51,7478.47,7458.92,7355.57,7434.99,7413.54,7377.16,7434.57,7405.02,7302.9,7409.96,7431.65,7422.17,7490.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.02,0.01,7302.9,7478.47,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.02,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.0,0.02,0.02,0.01,7478.47,GRU -2024-04-09,7508.91,7451.4,7454.91,7495.67,7458.67,7404.45,7504.04,7508.22,7535.45,7498.07,7362.18,7500.56,7456.07,7436.46,7490.4,7479.82,7307.45,7470.89,7473.05,7482.98,7507.5,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.01,0.0,0.01,0.02,0.0,0.0,0.03,0.01,7307.45,7535.45,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.01,0.0,0.01,0.02,0.0,0.0,0.03,0.0,7508.22,RNN -2024-04-10,7525.01,7463.69,7471.0,7599.2,7480.64,7407.01,7494.6,7518.41,7543.95,7529.9,7380.28,7529.39,7474.85,7472.81,7508.65,7484.08,7312.89,7512.26,7487.67,7500.6,7497.5,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.02,0.02,0.0,0.01,0.03,0.0,7312.89,7599.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.02,0.0,0.01,0.02,0.0,7494.6,PatchTST -2024-04-11,7504.87,7435.27,7455.34,7541.44,7475.08,7402.14,7456.31,7507.48,7525.09,7529.47,7398.46,7516.28,7430.02,7497.04,7500.72,7442.53,7317.92,7486.73,7485.56,7459.59,7497.5,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.0,0.02,0.0,7317.92,7541.44,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.02,0.0,7497.04,DLinear -2024-04-12,7479.46,7435.88,7455.09,7449.45,7465.52,7413.95,7465.76,7506.2,7527.13,7527.37,7413.39,7518.23,7412.17,7521.26,7487.64,7408.6,7322.92,7468.34,7477.26,7416.74,7506.67,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.02,0.0,7322.92,7527.37,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.02,0.01,7506.2,RNN -2024-04-15,7472.32,7395.35,7463.68,7434.46,7464.76,7388.03,7470.02,7506.19,7537.05,7531.6,7428.35,7524.9,7414.0,7503.56,7498.12,7387.92,7327.57,7459.92,7479.79,7412.79,7482.5,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.0,7327.57,7537.05,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.0,7479.79,TiDE -2024-04-16,7445.28,7384.64,7424.47,7387.4,7448.83,7379.28,7438.86,7474.01,7498.57,7514.49,7421.47,7486.23,7387.0,7473.29,7463.03,7381.95,7330.04,7305.22,7465.68,7344.09,7495.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.02,0.02,7305.22,7514.49,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.01,0.02,0.01,0.02,0.02,0.01,0.0,0.01,0.02,0.03,7498.57,GRU -2024-04-17,7446.2,7378.36,7448.03,7381.32,7454.08,7369.86,7440.14,7500.86,7526.87,7516.48,7420.73,7502.13,7377.72,7467.53,7480.13,7386.93,7332.3,7268.14,7470.71,7356.98,7497.5,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.02,0.02,7268.14,7526.87,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.02,0.02,0.02,0.01,0.02,0.01,0.0,0.02,0.02,0.03,7500.86,RNN -2024-04-18,7457.83,7363.61,7444.84,7383.72,7460.04,7373.79,7419.24,7500.61,7521.25,7508.95,7418.34,7495.19,7396.12,7451.62,7494.75,7389.4,7335.15,7228.79,7474.29,7391.17,7497.5,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.03,7228.79,7521.25,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.01,0.02,0.01,0.01,0.01,0.02,0.01,0.0,0.02,0.02,0.04,7495.19,DilatedRNN -2024-04-19,7469.48,7391.86,7446.51,7398.17,7463.59,7377.7,7399.12,7494.52,7524.0,7505.1,7416.04,7489.72,7404.43,7461.7,7489.8,7391.95,7339.46,7183.49,7469.89,7418.57,7527.5,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.02,0.04,7183.49,7524.0,0.01,0.0,0.01,0.01,0.02,0.01,0.01,0.01,0.0,0.01,0.02,0.01,0.02,0.02,0.02,0.01,0.0,0.02,0.02,0.05,7524.0,GRU -2024-04-22,7496.98,7406.58,7488.83,7424.42,7488.17,7396.38,7437.13,7540.59,7567.34,7522.83,7419.43,7523.84,7447.05,7465.61,7533.4,7406.44,7344.58,7230.71,7495.75,7421.88,7527.5,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.02,0.04,7230.71,7567.34,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.01,0.02,0.01,0.01,0.02,0.02,0.01,0.0,0.01,0.02,0.04,7523.84,DilatedRNN -2024-04-23,7500.82,7452.72,7481.53,7395.13,7502.22,7423.83,7450.05,7529.01,7554.42,7525.62,7422.18,7519.67,7474.19,7493.49,7518.56,7409.24,7349.08,7271.15,7508.02,7407.19,7510.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.01,0.02,0.03,7271.15,7554.42,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.02,0.02,0.03,7508.02,TiDE -2024-04-24,7497.27,7500.77,7459.8,7455.1,7496.63,7431.4,7476.97,7509.26,7532.07,7519.69,7425.21,7500.56,7480.36,7501.79,7492.2,7399.97,7354.25,7295.49,7501.93,7410.32,7487.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.0,0.01,0.02,0.03,7295.49,7532.07,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.0,0.0,0.02,0.03,7492.2,NLinear -2024-04-25,7494.2,7521.97,7429.77,7471.33,7478.92,7441.51,7488.24,7489.95,7505.15,7494.85,7433.35,7474.95,7478.76,7484.28,7477.99,7392.8,7358.79,7279.72,7481.18,7420.67,7487.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.0,0.0,0.02,0.03,7279.72,7521.97,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.0,0.0,0.02,0.03,7488.24,PatchTST -2024-04-26,7487.55,7493.94,7433.02,7460.56,7472.8,7448.11,7513.83,7482.12,7512.5,7489.62,7445.96,7481.97,7489.84,7486.69,7476.71,7395.86,7361.77,7288.1,7477.9,7450.74,7502.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.02,0.03,7288.1,7513.83,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.0,0.01,0.02,0.03,7493.94,Informer -2024-04-28,7492.31,7527.77,7453.0,7454.06,7481.64,7470.31,7526.84,7501.31,7531.63,7497.66,7466.42,7499.28,7485.05,7472.34,7496.09,7403.69,7365.99,7287.76,7486.34,7491.89,7527.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.02,0.03,7287.76,7531.63,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.02,0.01,0.01,0.0,0.01,0.02,0.03,7527.77,Informer -2024-04-29,7498.57,7548.47,7486.26,7431.35,7495.67,7531.22,7528.19,7533.16,7564.23,7521.28,7480.11,7535.52,7457.69,7502.54,7512.45,7415.22,7369.91,7304.96,7503.48,7512.67,7537.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.0,0.01,0.02,0.03,7304.96,7564.23,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.02,0.0,0.01,0.0,0.01,0.02,0.03,7535.52,DilatedRNN -2024-04-30,7495.75,7592.61,7496.31,7370.04,7501.83,7521.1,7525.03,7545.76,7570.16,7542.07,7482.28,7551.96,7438.24,7535.52,7529.03,7422.59,7374.53,7279.18,7509.82,7520.88,7532.5,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.02,0.02,0.03,7279.18,7592.61,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.01,0.0,0.02,0.02,0.03,7535.52,DLinear -2024-05-06,7486.98,7575.81,7488.82,7356.33,7496.14,7475.81,7510.93,7539.36,7560.82,7539.02,7472.66,7538.92,7425.33,7523.91,7531.51,7425.09,7380.07,7292.73,7510.87,7504.35,7502.5,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.02,0.01,0.03,7292.73,7575.81,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.0,0.02,0.02,0.03,7504.35,DeepNPTS -2024-05-07,7459.47,7522.92,7446.9,7293.06,7472.45,7416.02,7473.61,7500.68,7517.77,7513.82,7461.0,7495.62,7401.71,7499.71,7493.95,7423.25,7385.97,7269.02,7482.75,7427.8,7475.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.02,0.01,0.03,7269.02,7522.92,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.02,0.01,0.03,7473.61,PatchTST -2024-05-08,7452.1,7498.92,7412.88,7266.95,7451.0,7393.7,7456.99,7466.12,7491.15,7492.59,7451.26,7466.67,7400.62,7459.54,7476.41,7422.86,7391.94,7251.08,7457.2,7413.32,7512.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.01,0.02,0.01,0.03,7251.08,7498.92,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.02,0.01,0.0,0.03,0.02,0.03,7498.92,Informer -2024-05-09,7507.17,7481.83,7470.25,7276.28,7474.45,7402.05,7487.83,7516.03,7551.66,7514.2,7457.1,7515.92,7427.3,7461.2,7499.92,7434.42,7398.46,7258.22,7479.38,7489.75,7505.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.03,0.01,0.03,7258.22,7551.66,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.03,0.01,0.03,7507.17,NHITS -2024-05-10,7513.2,7490.87,7449.28,7300.66,7485.6,7421.4,7469.69,7498.91,7523.2,7512.77,7449.59,7501.96,7431.21,7468.93,7501.23,7437.45,7405.6,7252.87,7475.75,7494.29,7477.5,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.03,0.01,0.03,7252.87,7523.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.02,0.01,0.03,7475.75,TiDE -2024-05-11,7483.99,7472.65,7416.67,7318.22,7474.62,7444.24,7451.12,7474.21,7495.17,7500.56,7435.79,7469.77,7434.19,7468.85,7475.62,7438.88,7412.61,7258.36,7463.97,7463.24,7475.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.02,0.01,0.03,7258.36,7500.56,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.02,0.01,0.03,7474.62,TSMixer -2024-05-13,7492.02,7469.96,7417.52,7332.23,7473.29,7459.2,7460.44,7485.61,7499.11,7486.36,7435.56,7462.95,7449.81,7487.18,7477.07,7448.36,7419.8,7278.9,7470.81,7462.39,7485.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.02,0.01,0.03,7278.9,7499.11,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.02,0.01,0.03,7485.61,RNN -2024-05-14,7485.84,7471.29,7430.62,7342.64,7479.87,7489.41,7462.96,7479.66,7511.71,7483.63,7457.97,7468.02,7482.82,7489.58,7495.8,7450.75,7428.02,7325.6,7475.31,7470.97,7507.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.02,7325.6,7511.71,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.02,0.01,0.02,7511.71,GRU -2024-05-15,7494.66,7527.76,7462.12,7357.79,7498.05,7496.8,7487.66,7508.05,7541.39,7503.29,7486.74,7493.37,7518.69,7500.46,7501.49,7471.96,7432.85,7329.11,7495.48,7474.04,7510.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.02,7329.11,7541.39,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.01,0.02,7508.05,RNN -2024-05-16,7486.32,7547.16,7461.93,7375.92,7505.98,7500.59,7506.94,7517.17,7536.9,7516.66,7504.73,7500.54,7524.96,7515.96,7497.25,7475.93,7436.31,7369.26,7509.39,7487.81,7525.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.02,7369.26,7547.16,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.01,0.02,7524.96,MLP -2024-05-17,7495.27,7547.66,7484.75,7363.66,7512.85,7512.16,7527.19,7534.39,7561.08,7537.95,7517.79,7529.81,7511.87,7526.4,7519.39,7487.89,7438.31,7337.0,7520.01,7495.98,7587.5,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.02,0.01,0.02,7337.0,7561.08,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.03,0.02,0.03,7561.08,GRU -2024-05-20,7560.91,7627.52,7571.38,7382.25,7551.36,7540.14,7599.44,7615.53,7644.78,7640.54,7541.9,7609.56,7537.17,7538.88,7591.33,7608.87,7440.06,7344.79,7572.66,7580.6,7670.0,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.02,0.02,0.03,7344.79,7644.78,0.01,0.01,0.01,0.02,0.01,0.01,0.01,0.02,0.0,0.01,0.01,0.01,0.02,0.01,0.02,0.02,0.0,0.04,0.03,0.04,7644.78,GRU -2024-05-21,7658.46,7702.18,7684.89,7412.29,7615.56,7572.75,7679.05,7723.06,7740.26,7767.59,7554.82,7719.04,7600.38,7599.58,7671.84,7656.84,7442.83,7387.49,7645.65,7667.59,7677.5,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.03,0.03,0.04,7387.49,7767.59,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.02,0.01,0.03,0.03,0.04,7679.05,PatchTST -2024-05-22,7658.66,7694.97,7674.66,7429.14,7638.08,7568.24,7632.54,7712.51,7717.9,7771.7,7542.16,7724.91,7588.67,7629.96,7679.33,7656.76,7446.85,7412.06,7659.85,7672.34,7677.5,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.02,0.01,0.03,0.03,0.03,7412.06,7771.7,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.02,0.01,0.03,0.03,0.03,7679.33,NLinear -2024-05-23,7642.4,7676.73,7676.06,7435.77,7634.23,7552.03,7612.69,7712.66,7718.55,7767.44,7536.28,7722.24,7566.67,7651.84,7671.41,7581.26,7452.62,7439.62,7656.34,7653.24,7645.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.02,0.03,0.02,0.03,7435.77,7767.44,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.02,0.03,0.03,0.03,7642.4,NHITS -2024-05-24,7609.85,7677.56,7617.06,7413.75,7605.9,7513.65,7579.41,7654.46,7660.86,7738.27,7534.54,7674.64,7539.22,7650.42,7640.53,7529.25,7459.19,7459.08,7620.13,7598.74,7630.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.02,0.03,0.02,0.02,7413.75,7738.27,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.02,0.01,0.01,0.03,0.02,0.02,7620.13,TiDE -2024-05-27,7602.61,7604.74,7602.11,7442.68,7591.02,7509.58,7585.75,7619.81,7655.25,7679.82,7535.6,7648.6,7529.26,7610.52,7622.08,7536.4,7467.0,7448.39,7606.81,7607.49,7650.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.02,0.02,0.02,7442.68,7679.82,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.02,0.01,0.02,0.01,0.0,0.03,0.02,0.03,7648.6,DilatedRNN -2024-05-28,7621.53,7579.11,7632.67,7460.22,7604.76,7536.8,7604.37,7666.65,7688.61,7713.19,7536.61,7676.49,7535.3,7602.96,7628.69,7561.36,7475.23,7472.05,7623.2,7637.36,7690.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.02,0.02,0.02,7460.22,7713.19,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.02,0.02,0.02,0.02,0.0,0.03,0.03,0.03,7688.61,GRU -2024-05-29,7660.31,7564.95,7690.2,7542.13,7642.38,7556.52,7635.98,7722.5,7740.62,7758.82,7553.88,7726.35,7564.89,7633.76,7662.29,7584.71,7483.88,7537.41,7655.15,7643.97,7690.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.02,0.02,0.02,7483.88,7758.82,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.02,0.01,0.02,0.01,0.02,0.02,0.01,0.02,0.03,0.02,7690.2,LSTM -2024-05-30,7659.5,7553.99,7679.95,7592.99,7652.8,7541.2,7625.44,7716.96,7723.41,7779.81,7572.33,7718.33,7566.44,7647.5,7675.07,7605.57,7492.21,7596.18,7666.5,7566.29,7716.67,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.02,0.01,0.02,0.01,0.02,0.01,7492.21,7779.81,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.02,0.02,0.02,0.01,0.02,0.02,0.01,0.02,0.03,0.02,7716.96,RNN -2024-05-31,7676.04,7592.94,7721.02,7620.88,7669.84,7558.63,7640.45,7755.05,7767.45,7825.41,7589.97,7745.55,7604.26,7679.07,7714.9,7641.48,7499.87,7620.5,7694.39,7561.95,7740.0,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.0,0.02,0.01,0.02,0.01,0.02,0.01,7499.87,7825.41,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.02,0.02,0.02,0.01,0.02,0.02,0.01,0.02,0.03,0.02,7745.55,DilatedRNN -2024-06-03,7690.19,7602.5,7744.59,7643.27,7692.21,7576.14,7643.83,7774.9,7783.75,7838.53,7598.89,7768.8,7652.97,7688.61,7744.76,7658.11,7507.05,7660.01,7713.29,7635.55,7720.0,0.01,0.01,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.0,0.01,0.01,0.02,0.01,0.02,0.0,7507.05,7838.53,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.02,0.01,0.01,0.01,0.02,0.02,0.02,0.01,0.03,0.01,7713.29,TiDE -2024-06-04,7676.58,7604.72,7709.75,7625.74,7686.42,7586.88,7633.54,7732.22,7745.49,7822.4,7601.66,7739.83,7638.93,7670.32,7704.17,7640.85,7512.9,7624.79,7702.71,7650.31,7650.0,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.02,0.01,0.02,0.01,7512.9,7822.4,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.01,0.02,0.0,0.02,0.0,7650.31,DeepNPTS -2024-06-05,7617.42,7585.56,7608.16,7521.1,7636.97,7592.04,7619.76,7642.54,7645.94,7710.69,7592.1,7655.94,7600.55,7656.55,7622.66,7582.8,7516.58,7580.1,7640.7,7558.49,7620.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7516.58,7710.69,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,7619.76,PatchTST -2024-06-06,7627.55,7601.65,7581.97,7498.69,7602.71,7600.4,7617.53,7613.13,7635.79,7570.58,7579.57,7631.12,7591.91,7629.57,7600.04,7577.28,7519.33,7549.89,7594.44,7584.87,7600.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.02,0.01,0.01,7498.69,7635.79,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7600.04,NLinear -2024-06-07,7620.12,7576.07,7555.8,7496.98,7587.86,7584.3,7616.1,7584.17,7614.55,7535.23,7568.33,7605.75,7595.99,7578.05,7587.0,7574.39,7522.1,7512.69,7578.68,7597.59,7606.67,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.02,0.01,0.01,7496.98,7620.12,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,7605.75,DilatedRNN -2024-06-11,7615.42,7561.42,7572.2,7494.23,7592.89,7608.43,7623.98,7598.93,7636.73,7560.02,7573.55,7615.37,7596.81,7575.98,7597.5,7578.83,7525.05,7527.02,7589.47,7608.48,7620.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.02,0.01,0.01,7494.23,7636.73,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.02,0.01,0.01,7623.98,PatchTST -2024-06-12,7611.62,7553.93,7591.31,7496.85,7606.43,7639.72,7624.18,7636.33,7654.1,7637.44,7589.62,7633.55,7569.36,7605.97,7609.05,7591.22,7527.96,7539.77,7601.45,7616.41,7673.33,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.0,0.02,0.01,0.01,7496.85,7654.1,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.02,0.01,0.01,0.01,0.0,0.01,0.0,0.02,0.02,0.02,7654.1,GRU -2024-06-13,7655.61,7603.46,7674.73,7519.39,7641.07,7638.81,7672.45,7713.7,7731.49,7805.59,7621.73,7705.76,7603.03,7659.96,7673.02,7621.81,7532.56,7584.4,7645.12,7648.14,7690.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.02,0.02,0.02,0.01,7519.39,7805.59,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.02,0.02,0.01,7674.73,LSTM -2024-06-14,7662.58,7670.16,7685.94,7575.84,7660.44,7647.96,7682.6,7725.29,7731.85,7828.61,7646.61,7720.59,7639.52,7679.22,7692.25,7660.54,7536.97,7625.65,7665.64,7660.87,7690.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.02,0.0,7536.97,7828.61,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.02,0.01,0.02,0.01,7692.25,NLinear -2024-06-17,7671.29,7647.84,7684.84,7597.49,7665.59,7665.5,7691.83,7715.76,7728.39,7830.33,7664.51,7720.84,7657.3,7667.72,7690.86,7643.12,7541.43,7646.18,7679.22,7629.43,7656.67,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.02,0.01,0.02,0.0,7541.43,7830.33,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.02,0.0,7657.3,MLP -2024-06-18,7654.17,7665.9,7629.33,7606.86,7644.06,7636.13,7666.63,7667.54,7672.72,7798.64,7652.07,7680.42,7619.72,7641.58,7656.95,7592.23,7545.62,7627.04,7657.63,7638.64,7656.67,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.02,0.01,0.01,0.0,7545.62,7798.64,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.02,0.01,0.01,0.0,7656.95,NLinear -2024-06-19,7727.11,7640.08,7638.93,7578.59,7636.68,7591.02,7659.72,7664.19,7690.23,7756.19,7637.89,7681.55,7618.03,7634.64,7654.33,7591.34,7548.8,7635.99,7636.73,7637.2,7670.0,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.02,0.02,0.01,0.0,0.02,0.02,0.01,7548.8,7756.19,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.02,0.0,7664.19,RNN -2024-06-20,7756.39,7659.25,7656.39,7564.65,7645.7,7595.62,7650.33,7687.96,7706.14,7773.77,7634.08,7701.54,7642.69,7644.28,7666.27,7593.11,7552.24,7671.8,7632.11,7650.3,7680.0,0.01,0.01,0.02,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.02,0.02,0.0,0.02,0.03,0.01,7552.24,7773.77,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.02,0.02,0.0,7687.96,RNN -2024-06-21,7726.67,7685.7,7669.65,7563.31,7659.75,7610.74,7654.26,7698.64,7717.54,7814.91,7636.12,7712.27,7668.85,7668.87,7677.59,7596.45,7556.53,7678.47,7647.91,7660.17,7680.0,0.01,0.0,0.01,0.01,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.02,0.02,0.01,0.01,0.02,0.02,0.01,7556.53,7814.91,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.02,0.02,0.02,0.0,7678.47,MLPMultivariate -2024-06-24,7694.64,7672.12,7667.38,7570.59,7668.28,7625.21,7646.98,7705.2,7713.55,7819.28,7629.2,7707.03,7645.71,7680.38,7684.28,7592.7,7561.85,7657.72,7666.29,7648.92,7680.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.02,0.02,0.02,0.0,7561.85,7819.28,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.02,0.01,0.02,0.0,7680.38,DLinear -2024-06-25,7656.97,7685.36,7667.81,7584.83,7669.49,7652.55,7639.3,7702.86,7714.04,7804.54,7638.43,7698.53,7635.15,7695.28,7680.91,7592.76,7567.32,7651.89,7677.07,7634.02,7693.33,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.02,0.01,0.01,0.0,7567.32,7804.54,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.01,7695.28,DLinear -2024-06-26,7663.05,7700.56,7688.21,7592.06,7675.82,7671.93,7645.04,7717.94,7733.98,7810.16,7655.06,7714.51,7645.3,7714.43,7695.17,7603.42,7572.47,7672.88,7691.75,7632.18,7716.67,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.02,0.01,0.01,0.0,7572.47,7810.16,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.02,0.01,7717.94,RNN -2024-06-27,7677.4,7733.3,7717.34,7581.76,7688.27,7684.8,7672.33,7746.99,7762.31,7828.62,7679.04,7743.03,7653.37,7703.94,7708.81,7629.43,7577.01,7688.28,7699.69,7625.66,7723.33,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.02,0.01,0.01,0.0,7577.01,7828.62,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.02,0.02,0.0,7717.34,LSTM -2024-06-28,7677.32,7757.11,7720.51,7600.88,7694.57,7678.53,7677.87,7750.27,7761.7,7842.71,7690.04,7751.73,7651.4,7685.27,7712.89,7630.63,7581.37,7685.2,7704.47,7616.63,7730.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.02,0.01,0.01,0.0,7581.37,7842.71,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.02,0.01,7720.51,LSTM -2024-07-01,7707.93,7783.31,7729.01,7658.78,7698.83,7678.79,7708.14,7757.56,7769.41,7858.08,7695.38,7759.5,7666.04,7688.93,7720.96,7649.1,7586.13,7704.59,7715.52,7664.76,7763.33,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.0,0.02,0.01,0.02,0.0,7586.13,7858.08,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.01,7759.5,DilatedRNN -2024-07-02,7751.55,7828.43,7773.13,7771.68,7722.98,7680.78,7739.17,7797.09,7808.54,7880.75,7688.06,7793.55,7695.02,7722.0,7756.72,7715.2,7592.57,7759.12,7744.77,7741.74,7780.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.02,0.0,0.02,0.0,7592.57,7880.75,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.0,0.02,0.0,7773.13,LSTM -2024-07-03,7762.43,7821.1,7785.83,7768.14,7737.08,7688.67,7737.38,7805.72,7814.84,7884.52,7671.07,7805.74,7699.38,7736.65,7780.01,7781.53,7600.68,7781.87,7747.47,7776.13,7800.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.02,0.0,0.02,0.0,7600.68,7884.52,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.02,0.01,0.0,0.03,0.0,7805.72,RNN -2024-07-04,7786.08,7820.99,7808.32,7778.61,7757.09,7698.67,7767.39,7826.63,7834.39,7886.31,7664.84,7824.24,7716.39,7755.69,7793.56,7744.54,7607.4,7810.21,7767.25,7795.52,7800.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.02,0.01,0.0,0.02,0.0,7607.4,7886.31,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.02,0.01,0.0,0.02,0.0,7795.52,DeepNPTS -2024-07-05,7786.85,7820.6,7802.07,7857.05,7765.0,7704.08,7775.88,7824.34,7824.23,7867.37,7675.64,7822.93,7717.42,7771.03,7787.48,7722.59,7614.78,7784.03,7781.0,7773.13,7766.67,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.0,7614.78,7867.37,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.0,7765.0,TSMixer -2024-07-08,7750.86,7775.85,7756.63,7790.97,7745.48,7728.45,7735.1,7771.43,7782.12,7830.41,7681.79,7783.1,7680.52,7766.86,7756.73,7673.49,7623.7,7743.8,7760.92,7733.4,7746.67,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.02,0.0,7623.7,7830.41,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.02,0.0,7745.48,TSMixer -2024-07-09,7722.96,7771.44,7738.08,7700.89,7723.15,7740.89,7719.52,7758.3,7770.58,7808.82,7683.28,7764.36,7650.72,7738.94,7735.36,7674.87,7632.98,7718.29,7736.34,7708.61,7720.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.0,7632.98,7808.82,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.01,0.0,7719.52,PatchTST -2024-07-10,7678.34,7688.29,7704.44,7589.11,7701.77,7712.37,7697.39,7727.91,7738.12,7775.17,7671.93,7736.38,7618.9,7709.76,7703.35,7668.79,7641.77,7677.77,7704.73,7692.53,7693.33,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.0,0.0,7589.11,7775.17,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7692.53,DeepNPTS -2024-07-11,7648.69,7674.48,7673.89,7581.47,7676.21,7708.96,7680.71,7693.04,7711.88,7753.58,7666.8,7706.51,7619.0,7682.29,7677.71,7681.14,7649.18,7661.22,7672.21,7680.78,7686.67,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.0,7581.47,7753.58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.0,0.0,7682.29,DLinear -2024-07-12,7669.61,7650.62,7671.48,7621.69,7665.51,7658.81,7675.2,7701.66,7714.63,7765.84,7667.84,7701.91,7626.23,7678.42,7672.11,7680.77,7656.73,7648.62,7658.77,7681.25,7680.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.0,0.0,7621.69,7765.84,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.0,0.0,7680.77,TFT -2024-07-15,7674.74,7636.96,7662.9,7660.4,7664.41,7649.86,7662.4,7692.31,7707.55,7758.14,7666.8,7694.71,7642.93,7675.73,7670.65,7683.32,7663.66,7658.05,7658.65,7685.69,7680.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,7636.96,7758.14,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,7683.32,TFT -2024-07-16,7651.22,7665.87,7666.36,7679.77,7669.12,7662.39,7662.32,7694.89,7712.67,7768.03,7660.25,7693.56,7646.65,7667.06,7679.83,7685.51,7667.3,7657.69,7669.74,7726.9,7666.67,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.0,7646.65,7768.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,7666.36,LSTM -2024-07-17,7637.9,7660.13,7645.18,7660.46,7665.03,7655.32,7661.53,7680.92,7691.54,7757.11,7655.57,7673.35,7649.5,7651.11,7662.8,7670.56,7667.2,7655.92,7665.27,7701.17,7660.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.0,7637.9,7757.11,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,7660.13,Informer -2024-07-18,7661.33,7666.53,7639.5,7683.98,7661.71,7662.09,7657.96,7670.44,7689.36,7759.37,7647.55,7671.03,7659.78,7652.43,7661.6,7657.73,7666.66,7654.79,7657.27,7689.07,7656.67,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,7639.5,7759.37,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,7657.27,TiDE -2024-07-19,7665.03,7654.06,7635.24,7668.78,7662.77,7660.37,7654.84,7669.45,7686.07,7754.96,7643.93,7669.79,7665.58,7643.64,7663.61,7649.73,7666.01,7657.81,7657.13,7685.6,7656.67,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,7635.24,7754.96,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,7657.13,TiDE -2024-07-22,7649.73,7644.27,7636.49,7644.6,7665.12,7666.42,7654.45,7668.51,7688.3,7764.49,7644.52,7672.99,7672.46,7658.74,7658.42,7642.28,7666.42,7664.4,7656.29,7667.09,7640.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.0,7636.49,7764.49,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.0,7642.28,TFT -2024-07-23,7615.23,7645.56,7610.57,7655.94,7652.1,7662.34,7634.09,7645.28,7662.61,7733.56,7646.66,7650.71,7654.92,7659.24,7642.47,7624.17,7666.82,7659.42,7638.33,7656.65,7623.33,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.02,0.01,0.01,0.01,7610.57,7733.56,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,7624.17,TFT -2024-07-24,7597.53,7650.06,7590.27,7719.11,7634.82,7637.72,7616.98,7624.78,7646.08,7661.27,7642.03,7625.71,7641.44,7656.11,7626.19,7609.78,7665.78,7651.82,7623.34,7656.74,7623.33,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.02,0.01,0.01,7590.27,7719.11,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,7623.34,TiDE -2024-07-25,7608.65,7635.91,7593.96,7735.25,7627.76,7630.19,7616.05,7628.94,7653.01,7653.68,7631.24,7626.97,7640.3,7666.61,7627.16,7611.94,7663.56,7653.03,7621.39,7660.64,7616.67,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.02,0.01,0.01,7593.96,7735.25,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.0,7616.05,PatchTST -2024-07-26,7617.22,7632.98,7583.26,7719.98,7622.01,7624.53,7601.33,7616.61,7641.8,7622.3,7619.09,7615.34,7640.72,7653.16,7615.57,7609.45,7661.1,7643.86,7611.62,7667.77,7606.67,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,7583.26,7719.98,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,7609.45,TFT -2024-07-29,7632.45,7609.45,7571.18,7713.09,7611.68,7620.21,7594.82,7607.42,7631.3,7611.93,7608.13,7602.7,7638.04,7621.31,7607.55,7595.9,7657.72,7625.12,7607.82,7630.84,7593.33,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,7571.18,7713.09,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.02,0.01,0.0,7594.82,PatchTST -2024-07-30,7621.05,7605.67,7554.07,7693.67,7599.03,7601.6,7588.37,7595.38,7615.72,7591.75,7594.35,7583.41,7629.58,7602.19,7598.93,7574.65,7653.51,7600.72,7596.26,7617.22,7593.33,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,7554.07,7693.67,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,7594.35,BiTCN -2024-07-31,7587.46,7604.13,7557.67,7676.02,7593.74,7595.79,7589.72,7594.85,7621.85,7591.03,7581.07,7582.76,7605.31,7587.62,7602.61,7569.93,7649.69,7571.38,7593.87,7608.1,7583.33,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,7557.67,7676.02,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,7582.76,DilatedRNN -2024-08-01,7576.43,7594.77,7542.38,7676.91,7583.79,7575.55,7576.5,7581.67,7606.06,7578.42,7566.83,7567.78,7578.68,7581.86,7588.04,7561.38,7647.13,7543.93,7580.26,7601.32,7620.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,7542.38,7676.91,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,7606.06,GRU -2024-08-02,7616.25,7599.38,7601.53,7702.78,7607.2,7603.66,7609.55,7638.54,7668.72,7714.11,7574.54,7622.81,7587.85,7601.98,7625.29,7605.34,7647.13,7558.22,7606.16,7607.22,7623.33,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,7558.22,7714.11,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,7622.81,DilatedRNN -2024-08-05,7621.53,7592.47,7595.34,7698.87,7621.36,7617.55,7611.03,7638.07,7653.23,7735.43,7591.9,7625.9,7595.08,7607.4,7626.78,7606.12,7648.05,7577.72,7617.05,7605.43,7610.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,7577.72,7735.43,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.0,0.0,7611.03,PatchTST -2024-08-06,7614.82,7598.47,7578.08,7696.71,7616.64,7609.5,7607.33,7612.45,7636.6,7706.2,7602.73,7605.37,7605.46,7622.93,7609.92,7603.31,7648.18,7593.61,7621.72,7609.77,7596.67,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,7578.08,7706.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7598.47,Informer -2024-08-07,7600.42,7592.13,7560.49,7700.19,7603.22,7600.74,7594.67,7605.4,7620.27,7632.15,7602.99,7587.78,7602.43,7620.43,7599.1,7588.53,7647.25,7581.33,7607.23,7602.89,7586.67,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,7560.49,7700.19,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,7587.78,DilatedRNN -2024-08-08,7582.46,7579.27,7547.9,7691.99,7590.51,7583.07,7585.2,7581.87,7610.47,7580.54,7596.98,7572.43,7580.94,7599.9,7587.52,7592.21,7644.23,7643.29,7591.49,7595.2,7583.33,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7547.9,7691.99,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7583.07,TSMixerx -2024-08-09,7572.3,7579.08,7544.76,7685.98,7581.03,7579.99,7585.11,7579.56,7609.39,7579.0,7593.55,7571.36,7577.72,7584.52,7578.71,7580.27,7640.76,7661.68,7580.79,7613.95,7583.33,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.01,7544.76,7685.98,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7584.52,DLinear -2024-08-12,7565.19,7562.29,7545.41,7676.57,7579.53,7568.37,7583.51,7586.38,7610.8,7581.76,7591.14,7573.16,7571.12,7579.25,7574.68,7569.9,7637.37,7675.83,7579.89,7606.85,7586.67,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7545.41,7676.57,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7586.38,RNN -2024-08-13,7563.62,7566.02,7550.8,7670.48,7579.66,7581.39,7587.7,7591.05,7616.26,7595.69,7589.46,7581.39,7578.19,7586.37,7580.72,7572.45,7634.98,7685.1,7584.15,7605.62,7593.33,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.02,7550.8,7685.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7591.05,RNN -2024-08-14,7576.39,7581.33,7560.34,7675.32,7581.9,7581.96,7593.26,7601.19,7625.12,7601.06,7585.96,7582.96,7591.15,7583.67,7594.52,7589.33,7632.88,7686.85,7588.09,7596.56,7590.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7560.34,7686.85,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7589.33,TFT -2024-08-15,7577.48,7580.18,7554.1,7682.5,7580.27,7581.07,7588.06,7594.36,7617.3,7594.63,7577.61,7574.04,7585.9,7573.04,7589.19,7595.72,7630.35,7676.25,7584.21,7588.85,7583.33,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7554.1,7682.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7584.21,TiDE -2024-08-16,7575.3,7564.63,7545.8,7686.68,7575.85,7576.91,7580.0,7585.32,7609.72,7583.25,7570.84,7568.21,7574.59,7570.58,7584.68,7589.57,7627.38,7665.61,7577.83,7587.43,7583.33,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7545.8,7686.68,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7583.25,TCN -2024-08-19,7583.78,7564.47,7547.04,7693.78,7576.66,7590.53,7583.24,7587.57,7611.94,7580.78,7566.85,7568.77,7576.56,7563.43,7580.76,7592.1,7624.46,7672.57,7577.68,7592.79,7570.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7547.04,7693.78,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.01,7568.77,DilatedRNN -2024-08-20,7572.15,7547.01,7526.81,7671.45,7568.6,7580.24,7569.61,7567.35,7591.95,7568.04,7561.53,7550.82,7574.94,7559.89,7564.28,7585.51,7621.06,7668.15,7564.85,7593.95,7570.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7526.81,7671.45,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7569.61,PatchTST -2024-08-21,7573.42,7547.32,7530.82,7666.64,7567.89,7575.96,7578.4,7571.05,7598.86,7567.66,7559.81,7551.3,7579.86,7556.56,7568.24,7577.28,7617.48,7658.24,7567.96,7595.48,7570.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7530.82,7666.64,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7571.05,RNN -2024-08-22,7573.07,7553.0,7529.61,7668.62,7570.14,7576.54,7578.19,7572.59,7597.05,7564.06,7557.67,7549.96,7582.79,7558.09,7571.36,7573.0,7613.63,7649.21,7570.82,7596.41,7590.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7529.61,7668.62,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,7596.41,DeepNPTS -2024-08-23,7591.57,7570.94,7560.14,7670.81,7585.53,7577.36,7595.08,7597.17,7628.08,7602.95,7563.0,7578.88,7588.25,7580.61,7588.53,7588.33,7610.53,7651.94,7586.39,7599.99,7583.33,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,7560.14,7670.81,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,7585.53,TSMixer -2024-08-26,7585.94,7559.82,7544.76,7661.83,7584.95,7583.38,7584.8,7588.16,7607.8,7587.92,7569.26,7568.6,7581.87,7588.27,7584.7,7584.03,7607.22,7649.99,7582.57,7594.95,7583.33,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,7544.76,7661.83,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,7583.38,TSMixerx -2024-08-27,7583.67,7562.52,7548.9,7658.7,7583.01,7588.14,7583.17,7588.62,7614.19,7590.1,7572.3,7571.03,7577.78,7596.16,7581.18,7582.94,7603.87,7646.62,7585.5,7587.22,7610.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,7548.9,7658.7,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,7614.19,GRU -2024-08-28,7608.75,7573.37,7587.12,7673.3,7599.94,7608.28,7606.73,7628.21,7652.47,7664.54,7591.65,7610.38,7596.44,7602.33,7610.44,7591.02,7601.62,7649.51,7604.31,7595.06,7610.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,7573.37,7673.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,7610.38,DilatedRNN -2024-08-29,7610.24,7581.95,7579.49,7667.2,7606.13,7611.64,7601.7,7614.17,7638.98,7657.09,7608.52,7604.72,7599.74,7592.46,7610.07,7593.12,7599.89,7651.06,7610.39,7598.34,7613.33,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,7579.49,7667.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,7614.17,RNN -2024-08-30,7614.03,7597.63,7586.96,7655.72,7609.34,7627.82,7609.66,7622.84,7647.73,7683.32,7625.2,7611.68,7604.11,7604.98,7607.4,7605.44,7598.84,7655.51,7616.44,7604.42,7606.67,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,7586.96,7683.32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,7607.4,NLinear -2024-09-02,7605.77,7607.48,7574.01,7657.55,7606.13,7623.34,7601.08,7617.14,7632.79,7647.45,7631.48,7601.88,7607.32,7602.92,7610.32,7598.03,7597.7,7649.4,7604.07,7593.73,7583.33,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,7574.01,7657.55,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.01,7574.01,LSTM -2024-09-03,7585.53,7591.18,7541.28,7616.14,7585.56,7597.08,7575.22,7577.4,7602.02,7584.32,7620.22,7566.19,7593.28,7584.09,7584.79,7569.87,7596.02,7632.57,7579.08,7589.84,7562.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,7541.28,7632.57,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,7566.19,DilatedRNN -2024-09-04,7568.37,7571.94,7515.98,7610.57,7565.79,7581.26,7562.25,7558.08,7581.85,7545.27,7604.56,7535.46,7576.39,7572.94,7565.45,7557.33,7593.59,7618.83,7560.98,7584.89,7535.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,7515.98,7618.83,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,7535.46,DilatedRNN -2024-09-05,7543.76,7554.78,7480.27,7596.32,7542.46,7560.43,7542.41,7526.06,7549.62,7514.26,7580.6,7496.51,7554.42,7552.65,7529.62,7533.45,7589.9,7609.7,7536.19,7604.62,7475.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7480.27,7609.7,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.02,0.01,0.01,0.01,0.01,0.01,0.02,0.02,0.02,7480.27,LSTM -2024-09-06,7486.44,7535.38,7399.34,7579.63,7490.15,7530.32,7484.73,7455.8,7477.09,7467.9,7540.47,7420.37,7514.24,7518.1,7469.82,7508.77,7582.87,7581.29,7483.85,7606.65,7442.5,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.02,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.01,7399.34,7606.65,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.01,0.01,0.02,0.01,0.01,0.01,0.01,0.0,0.02,0.02,0.02,7455.8,RNN -2024-09-09,7470.01,7515.88,7364.62,7562.42,7446.27,7498.53,7460.67,7431.29,7457.63,7439.77,7496.35,7380.39,7490.04,7478.05,7433.33,7489.86,7574.18,7557.17,7447.27,7583.34,7422.5,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.02,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7364.62,7583.34,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.02,0.01,0.01,0.01,0.01,0.0,0.02,0.02,0.02,7431.29,RNN -2024-09-10,7451.33,7504.81,7339.71,7541.21,7420.48,7477.1,7438.77,7409.71,7440.34,7417.87,7462.64,7355.09,7458.54,7445.15,7421.15,7475.16,7564.85,7527.78,7425.45,7562.5,7412.5,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.02,0.01,7339.71,7564.85,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.02,0.01,0.01,0.01,0.01,0.0,0.02,0.02,0.02,7409.71,RNN -2024-09-11,7430.97,7491.05,7330.97,7519.37,7404.28,7462.26,7420.89,7398.85,7436.14,7408.15,7431.14,7340.45,7432.55,7418.98,7415.28,7464.61,7555.64,7498.71,7417.44,7535.0,7387.5,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.02,0.01,7330.97,7555.64,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.02,0.01,0.01,0.01,0.01,0.0,0.02,0.02,0.02,7398.85,RNN -2024-09-12,7398.0,7478.74,7303.67,7492.92,7382.31,7444.67,7391.09,7382.94,7411.65,7396.88,7397.14,7313.15,7402.38,7400.79,7386.94,7448.76,7545.54,7460.06,7395.3,7475.01,7380.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.02,0.01,7303.67,7545.54,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.0,0.02,0.02,0.01,7382.31,TSMixer -2024-09-13,7388.71,7460.03,7300.95,7464.13,7369.45,7424.93,7380.86,7379.25,7411.88,7392.85,7374.15,7309.57,7382.1,7401.54,7385.04,7438.55,7535.68,7439.73,7384.62,7442.51,7395.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.02,0.01,7300.95,7535.68,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.02,0.01,7392.85,TCN -2024-09-14,7403.95,7464.0,7318.92,7450.39,7380.9,7417.35,7390.87,7396.28,7429.07,7406.03,7371.11,7327.5,7375.1,7403.94,7403.98,7435.49,7527.23,7421.01,7393.19,7422.53,7375.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.02,0.0,7318.92,7527.23,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.02,0.01,7375.1,MLP -2024-09-18,7378.25,7450.51,7296.64,7420.48,7374.33,7397.35,7365.31,7369.65,7403.89,7405.31,7367.04,7307.74,7352.11,7382.56,7384.77,7418.46,7518.24,7376.68,7382.2,7412.61,7342.5,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.02,0.0,7296.64,7518.24,0.01,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.02,0.0,7352.11,MLP -2024-09-19,7345.8,7424.07,7273.03,7383.78,7351.39,7367.71,7348.02,7343.67,7377.78,7405.74,7364.49,7286.64,7340.79,7371.55,7346.23,7404.98,7508.11,7313.24,7357.08,7392.25,7325.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.01,0.01,0.02,0.0,7273.03,7508.11,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.02,0.0,7313.24,MLPMultivariate -2024-09-20,7331.34,7411.4,7261.47,7359.5,7333.1,7352.78,7340.24,7332.29,7366.28,7407.88,7366.1,7275.0,7339.29,7357.37,7336.36,7381.77,7497.51,7277.24,7337.64,7389.01,7340.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.02,0.01,7261.47,7497.51,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.02,0.01,7340.24,PatchTST -2024-09-23,7350.17,7409.47,7273.6,7360.28,7338.57,7354.65,7355.94,7342.36,7383.53,7413.67,7372.86,7285.44,7350.24,7359.13,7346.39,7390.63,7488.3,7253.74,7336.25,7363.92,7310.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.02,0.01,7253.74,7488.3,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.01,7285.44,DilatedRNN -2024-09-24,7334.33,7387.52,7250.34,7333.1,7321.56,7341.2,7323.07,7313.94,7351.58,7405.47,7369.32,7258.87,7332.21,7339.95,7319.59,7372.23,7478.05,7233.88,7309.53,7330.44,7280.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.02,0.01,7233.88,7478.05,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.01,0.03,0.01,7258.87,DilatedRNN -2024-09-25,7331.68,7358.95,7233.76,7294.82,7295.02,7325.46,7313.92,7295.67,7333.57,7389.7,7358.18,7239.93,7329.29,7318.18,7284.22,7352.1,7466.86,7208.89,7292.13,7300.88,7315.0,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.02,0.02,7208.89,7466.86,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.02,0.01,7313.92,PatchTST -2024-09-26,7383.23,7354.28,7258.63,7322.43,7311.57,7337.57,7346.59,7334.2,7368.18,7395.19,7360.13,7262.58,7348.37,7337.87,7321.63,7365.79,7457.88,7369.88,7315.96,7309.91,7310.0,0.01,0.01,0.01,0.01,0.0,0.0,0.02,0.01,0.0,0.02,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,7258.63,7457.88,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.02,0.01,7309.91,DeepNPTS -2024-09-27,7395.39,7342.84,7252.57,7349.44,7311.78,7332.55,7328.61,7316.26,7355.87,7383.46,7355.37,7250.85,7333.29,7314.8,7318.06,7363.19,7449.12,7426.95,7307.61,7323.36,7310.0,0.01,0.01,0.01,0.01,0.01,0.0,0.02,0.01,0.01,0.02,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.0,7250.85,7449.12,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.02,0.02,7311.78,TSMixer -2024-09-29,7404.69,7328.01,7255.95,7412.08,7310.44,7339.92,7335.89,7322.03,7360.7,7391.28,7348.53,7255.08,7324.23,7308.09,7314.78,7356.94,7440.11,7459.6,7314.78,7323.36,7365.0,0.01,0.01,0.01,0.01,0.01,0.0,0.02,0.01,0.01,0.02,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.0,0.0,0.01,7255.08,7459.6,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.01,7360.7,GRU -2024-09-30,7426.12,7341.11,7300.09,7456.35,7351.28,7360.36,7371.79,7386.81,7413.94,7423.46,7354.23,7305.8,7350.79,7337.02,7372.67,7375.01,7433.62,7526.69,7362.78,7362.27,7420.0,0.01,0.01,0.01,0.01,0.01,0.0,0.02,0.01,0.0,0.02,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.0,0.0,0.01,7300.09,7526.69,0.01,0.0,0.01,0.01,0.01,0.0,0.02,0.01,0.0,0.02,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.0,0.0,0.01,7423.46,TCN -2024-10-08,7406.37,7396.05,7363.97,7515.23,7401.51,7391.97,7409.69,7428.8,7463.3,7450.62,7382.85,7368.46,7376.8,7366.11,7426.31,7404.72,7429.4,7614.2,7412.17,7419.09,7602.5,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.03,7363.97,7614.2,0.02,0.02,0.03,0.03,0.03,0.03,0.03,0.03,0.02,0.03,0.03,0.02,0.03,0.03,0.03,0.03,0.02,0.01,0.02,0.0,7614.2,MLPMultivariate -2024-10-09,7577.33,7574.57,7625.6,7661.65,7544.47,7607.6,7631.74,7679.46,7699.37,7713.1,7580.36,7635.88,7586.54,7484.3,7607.45,7604.1,7429.48,7766.63,7573.71,7602.24,7602.5,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.02,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.02,0.02,7429.48,7766.63,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.02,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.02,0.02,7602.24,DeepNPTS -2024-10-10,7576.0,7601.33,7581.64,7719.31,7608.25,7677.35,7632.34,7639.99,7633.43,7766.71,7661.03,7634.72,7640.36,7542.83,7609.24,7617.74,7429.69,7786.16,7604.18,7602.37,7585.0,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.03,0.02,0.02,0.03,7429.69,7786.16,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.02,0.02,0.02,0.03,7581.64,LSTM -2024-10-11,7631.65,7624.48,7575.07,7766.2,7609.2,7714.27,7629.44,7599.98,7624.22,7788.7,7691.29,7618.88,7651.66,7572.18,7587.86,7623.61,7429.71,7764.22,7600.31,7584.85,7575.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.01,0.02,0.02,0.03,0.02,7429.71,7788.7,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.02,0.02,0.03,0.03,0.02,0.02,7575.07,LSTM -2024-10-12,7653.07,7598.33,7548.5,7847.1,7585.18,7706.53,7607.08,7603.88,7602.92,7740.27,7700.39,7589.1,7663.18,7571.87,7574.65,7629.57,7429.59,7716.69,7568.07,7574.84,7576.67,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.0,0.01,0.01,0.01,0.03,0.03,0.01,7429.59,7847.1,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.02,0.02,0.02,0.04,0.02,0.02,7574.84,DeepNPTS -2024-10-14,7691.78,7608.75,7548.03,7871.49,7552.13,7690.78,7583.72,7553.81,7607.37,7730.31,7696.8,7583.52,7677.65,7557.6,7562.15,7651.37,7429.44,7670.88,7546.73,7576.1,7565.0,0.02,0.02,0.02,0.02,0.01,0.0,0.01,0.02,0.01,0.02,0.01,0.02,0.0,0.01,0.0,0.0,0.01,0.02,0.03,0.0,7429.44,7871.49,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.02,0.02,0.02,0.04,0.02,0.01,7562.15,NLinear -2024-10-15,7639.02,7604.74,7524.41,7792.94,7538.07,7647.37,7550.89,7554.31,7587.3,7702.48,7678.15,7562.07,7640.33,7526.24,7536.04,7653.55,7429.02,7615.24,7536.11,7564.45,7527.5,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.02,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.02,0.03,0.0,7429.02,7792.94,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.02,0.02,0.02,0.02,0.04,0.01,0.01,7526.24,DLinear -2024-10-16,7560.16,7584.03,7472.19,7524.15,7510.13,7557.71,7500.61,7525.6,7538.71,7580.69,7620.62,7490.21,7571.23,7510.21,7483.04,7574.04,7427.96,7569.48,7510.9,7522.37,7477.5,0.01,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.02,0.0,7427.96,7620.62,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.01,0.02,0.01,0.01,0.01,0.01,7472.19,LSTM -2024-10-17,7497.05,7571.56,7406.36,7436.05,7462.23,7506.3,7433.55,7466.97,7484.21,7495.48,7551.6,7417.59,7502.19,7486.54,7451.01,7481.14,7425.86,7531.56,7464.61,7505.66,7467.5,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,7406.36,7571.56,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,7466.97,RNN -2024-10-18,7485.83,7576.53,7400.38,7405.13,7439.71,7492.0,7417.06,7460.72,7487.02,7473.88,7503.86,7368.89,7465.12,7435.8,7465.36,7460.77,7423.55,7513.17,7448.06,7457.38,7370.0,0.0,0.0,0.01,0.01,0.01,0.0,0.02,0.01,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,7368.89,7576.53,0.01,0.01,0.01,0.01,0.01,0.02,0.0,0.01,0.02,0.0,0.03,0.01,0.01,0.01,0.02,0.02,0.01,0.0,0.01,0.02,7368.89,DilatedRNN -2024-10-21,7344.19,7448.06,7278.98,7367.94,7369.4,7422.88,7311.05,7344.49,7375.82,7434.49,7406.12,7267.31,7378.31,7359.09,7361.07,7360.86,7418.39,7455.82,7359.83,7457.08,7362.5,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.02,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.02,7267.31,7457.08,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.01,7361.07,NLinear -2024-10-22,7325.96,7412.3,7285.18,7351.41,7343.75,7341.76,7318.72,7356.17,7398.84,7424.42,7348.14,7270.19,7359.63,7331.88,7344.37,7359.19,7413.08,7435.49,7339.82,7498.96,7346.67,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.02,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,7270.19,7498.96,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.02,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,7348.14,BiTCN -2024-10-23,7300.58,7353.2,7269.19,7345.42,7330.7,7285.19,7316.86,7347.0,7376.53,7407.48,7302.48,7257.8,7344.1,7307.03,7315.78,7345.47,7407.29,7416.71,7328.57,7479.02,7357.5,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.02,0.01,0.01,0.0,0.0,0.01,0.01,0.01,0.02,7257.8,7479.02,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.01,0.0,0.02,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.01,7353.2,Informer -2024-10-24,7327.36,7330.89,7282.53,7334.39,7335.02,7320.17,7344.86,7348.92,7396.87,7389.91,7289.94,7275.05,7332.82,7332.66,7343.01,7344.94,7401.82,7432.15,7340.73,7461.51,7352.5,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.02,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,7275.05,7461.51,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,7348.92,RNN -2024-10-25,7343.94,7307.17,7278.58,7353.38,7347.83,7335.72,7367.26,7362.14,7388.07,7392.13,7273.09,7271.2,7327.52,7320.14,7359.31,7334.4,7396.39,7461.64,7356.27,7405.27,7372.5,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.02,7271.2,7461.64,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.0,0.0,0.0,0.01,7367.26,PatchTST -2024-10-28,7348.35,7346.41,7299.95,7382.28,7373.17,7332.05,7394.25,7378.33,7413.72,7409.43,7278.91,7304.22,7355.17,7345.4,7380.76,7341.12,7391.81,7485.78,7385.35,7352.0,7373.33,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,0.02,7278.91,7485.78,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.02,7373.17,TSMixer -2024-10-29,7362.26,7347.62,7299.38,7403.71,7382.63,7372.1,7388.31,7376.75,7408.46,7425.13,7307.02,7317.3,7360.51,7394.13,7388.48,7339.41,7387.2,7506.54,7374.2,7368.01,7380.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.02,7299.38,7506.54,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.02,7382.63,TSMixer -2024-10-30,7368.73,7372.31,7309.24,7410.82,7385.75,7428.36,7376.78,7383.46,7418.25,7445.98,7354.4,7342.89,7363.36,7410.4,7387.01,7346.05,7383.02,7505.69,7359.37,7437.75,7330.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.02,7309.24,7505.69,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.02,0.01,0.01,0.02,7342.89,DilatedRNN -2024-10-31,7311.11,7337.7,7264.33,7417.51,7354.56,7416.21,7295.6,7331.68,7363.08,7446.37,7349.71,7310.97,7345.95,7400.75,7339.24,7304.52,7377.33,7490.8,7331.52,7393.39,7340.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.0,0.0,0.01,0.01,0.02,0.01,0.01,0.02,7264.33,7490.8,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.0,0.01,0.0,0.01,0.01,0.01,0.02,7339.24,NLinear -2024-11-01,7325.45,7340.67,7277.19,7403.85,7350.09,7391.7,7325.08,7346.34,7386.1,7448.67,7361.61,7319.58,7370.1,7385.94,7339.78,7319.82,7372.31,7485.72,7337.9,7388.84,7363.33,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.01,0.0,0.02,0.01,0.01,0.02,7277.19,7485.72,0.0,0.0,0.0,0.0,0.01,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.02,7361.61,BiTCN -2024-11-04,7380.95,7383.65,7292.43,7383.24,7362.64,7391.41,7347.38,7368.79,7402.84,7453.22,7370.16,7335.67,7402.01,7359.02,7359.95,7345.06,7368.38,7497.59,7359.38,7382.9,7360.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.02,7292.43,7497.59,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.02,7359.95,NLinear -2024-11-05,7384.76,7385.63,7289.12,7346.33,7359.37,7429.52,7351.29,7355.62,7396.87,7454.93,7371.16,7325.31,7409.42,7374.6,7372.07,7351.83,7364.63,7481.09,7351.21,7367.23,7357.5,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.01,7289.12,7481.09,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.01,0.0,0.01,0.0,0.0,0.02,7359.37,TSMixer 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+StemGNN,13746.6,117.246,94.793 diff --git a/juxitingdataset/predict.csv b/juxitingdataset/predict.csv index 7539745..a81870c 100644 --- 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 -1,2024-11-20,7337.594,7316.5264,7313.051,7342.468,7344.392,7306.8916,7350.6377,7378.303,7405.318,7418.5396,7321.0723,7320.571,7338.035,7387.8057,7361.405,7362.294,,7351.2534,7331.5254,7354.707,7376.7446 -1,2024-11-21,7317.166,7307.525,7316.9746,7328.6523,7332.2124,7313.822,7338.1885,7370.3306,7411.06,7428.9434,7317.8125,7327.2505,7336.689,7338.9487,7351.548,7367.47,,7347.7666,7312.2354,7317.218,7363.681 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b/juxitingdataset/价差类指标相关性直方分布图.png differ diff --git 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 index cd695cd..fbeb5aa 100644 Binary files a/juxitingdataset/价格类指标相关性直方分布图.png and b/juxitingdataset/价格类指标相关性直方分布图.png differ diff --git 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 index f34d4dc..a64dfb2 100644 Binary files a/juxitingdataset/供应类指标相关性直方分布图.png and b/juxitingdataset/供应类指标相关性直方分布图.png differ diff --git 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 index d33f8ea..92da4f4 100644 Binary files a/juxitingdataset/其他类指标相关性直方分布图.png and b/juxitingdataset/其他类指标相关性直方分布图.png differ diff --git a/juxitingdataset/历史价格-预测值.png b/juxitingdataset/历史价格-预测值.png index ce57214..4f43206 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/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 index 58cd0d8..d25bd64 100644 Binary files a/juxitingdataset/指标分类相关性总和的气泡图.png and b/juxitingdataset/指标分类相关性总和的气泡图.png differ diff --git a/juxitingdataset/指标数据.csv b/juxitingdataset/指标数据.csv index 5a06f49..13fa01a 100644 --- a/juxitingdataset/指标数据.csv +++ b/juxitingdataset/指标数据.csv @@ -760,14 +760,14 @@ ds,PP主力收盘价拟合残差/丙烷 CP M1,华南聚丙烯基差(折盘面 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diff --git 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 index 5a06f49..13fa01a 100644 --- 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 @@ -760,14 +760,14 @@ ds,PP主力收盘价拟合残差/丙烷 CP M1,华南聚丙烯基差(折盘面 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diff --git a/juxitingdataset/未填充的特征数据.csv b/juxitingdataset/未填充的特征数据.csv index d91e291..5482c12 100644 --- a/juxitingdataset/未填充的特征数据.csv +++ b/juxitingdataset/未填充的特征数据.csv @@ -760,14 +760,14 @@ ds,PP主力收盘价拟合残差/丙烷 CP M1,华南聚丙烯基差(折盘面 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diff --git a/juxitingdataset/模型评估.png b/juxitingdataset/模型评估.png index 88c9da1..b3d7c9b 100644 Binary files a/juxitingdataset/模型评估.png and b/juxitingdataset/模型评估.png differ diff --git a/juxitingdataset/预测值表格.png b/juxitingdataset/预测值表格.png index 9b106fc..b73d439 100644 Binary files a/juxitingdataset/预测值表格.png and b/juxitingdataset/预测值表格.png differ diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py index 8f0910b..d2beaa6 100644 --- a/models/nerulforcastmodels.py +++ b/models/nerulforcastmodels.py @@ -218,7 +218,7 @@ def ex_Model(df,horizon,input_size,train_steps,val_check_steps,early_stop_patien return nf_test_preds -# 计算预测评估指数 +# 原油计算预测评估指数 def model_losss(sqlitedb): global dataset # 预测数据处理 predict @@ -483,12 +483,10 @@ def model_losss(sqlitedb): plt.close() return model_results3 - - - -# 计算预测评估指数 +# 聚烯烃计算预测评估指数 def model_losss_juxiting(sqlitedb): global dataset + global rote 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] @@ -556,292 +554,6 @@ def model_losss_juxiting(sqlitedb): plt.close() - # 计算每个模型与最佳模型的绝对误差比例 - names = [] - for col in allmodelnames: - 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] - names.append(f'{col}-{most_model_name}-误差比例') - - # 设置阈值 rote - rote = 0.04 - names_df = df_combined3[names] - # names_df['rote'] = rote - def add_rote_column(row): - columns = [] - for r in names_df.columns: - if row[r] <= rote: - columns.append(r.split('-')[0]) - return pd.Series([columns], index=['columns']) - names_df['columns'] = names_df.apply(add_rote_column, axis=1) - - def add_upper_lower_bound(row): - print(row['columns']) - print(type(row['columns'])) - # 计算上边界值 - upper_bound = df_combined3.loc[row.name,row['columns']].max() - # 计算下边界值 - lower_bound = df_combined3.loc[row.name,row['columns']].min() - return pd.Series([lower_bound, upper_bound], index=['lower_bound', 'upper_bound']) - df_combined3[['upper_bound','lower_bound']] = names_df.apply(add_upper_lower_bound, axis=1) - - - - - # # 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 - # def find_min_max_within_quantile(row): - # true_value = row['y'] - # row.drop(['ds','y'], inplace=True) - # row = row.astype(float).round(2) - - # max_heap = [] - # min_heap = [] - # for col in row.index: - # # 对比真实值进行分类 - # if row[col] < true_value: - # heapq.heappush(min_heap, row[col]) - # elif row[col] > true_value: - # heapq.heappush(max_heap, -row[col]) # 使用负号来实现最大堆 - - # if len(max_heap) == 1: - # max_y = max_heap[0] - # elif len(max_heap) == 0: - # max_y = -min_heap[-1] - # else: - # max_y = heapq.nsmallest(2, max_heap)[1] - - # if len(min_heap) < 2 : - # min_y = -max_heap[-1] - # else: - # min_y = heapq.nsmallest(2, min_heap)[-1] - - - # # 获取最大和最小的值 - # q10 = min_y - # q90 = -max_y - - # # 获取最大和最小的模型名称 - # min_model = row[row == q10].idxmin() - # max_model = row[row == q90].idxmax() - - # # 设置上下界比例 - # rote = 1 - - # q10 = q10 * rote - # q90 = q90 * rote - - # logger.info(min_model,q10,max_model,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) - - #使用最佳五个模型进行绘图 - # 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) - - # # 通道使用模型评估前80%作为置信度 - # def find_min_max_within_quantile(row): - # row.drop(['ds','y'], inplace=True) - # row = row.astype(float).round(2) - - # row_sorted = row - # # 计算 10% 和 90% 位置的索引 - # index_10 = 0 - # index_90 = int(len(row_sorted) * 0.8) - # q10 = row_sorted[index_10] - # q90 = row_sorted[index_90] - # # 获取模型名称 - # 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']) - - # # 重新排列 - # df_combined3 = df_combined3[['ds','y'] + allmodelnames] - # # 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) - - - # # 通道使用预测模型的80%置信度 - # def find_min_max_within_quantile(row): - # row.drop(['ds','y'], inplace=True) - # row = row.astype(float).round(2) - - # row_sorted = row.sort_values(ascending=True).reset_index(drop=True) - # # 计算 10% 和 90% 位置的索引 - # index_10 = int(len(row_sorted) * 0.1) - # index_90 = int(len(row_sorted) * 0.9) - # q10 = row_sorted[index_10] - # q90 = row_sorted[index_90] - # # 获取模型名称 - # 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) - - - - - # # 计算波动率 - # df_combined3['volatility'] = df_combined3['y'].pct_change().round(4) - # # 计算近60日的波动率 10% 90%分位数 - # df_combined3['quantile_10'] = df_combined3['volatility'].rolling(60).quantile(0.1) - # df_combined3['quantile_90'] = df_combined3['volatility'].rolling(60).quantile(0.9) - # df_combined3 = df_combined3.round(4) - # # 计算分位数对应的价格 - # df_combined3['quantile_10_price'] = df_combined3['y'] * (1 + df_combined3['quantile_10']) - # df_combined3['quantile_90_price'] = df_combined3['y'] * (1 + df_combined3['quantile_90']) - - # # 遍历行 - # def find_min_max_within_quantile(row): - # # 获取分位数10%和90%的值 - # q10 = row['quantile_10_price'] - # q90 = row['quantile_90_price'] - - # # 判断flot值是否为空值 - # if pd.isna(q10) or pd.isna(q90): - # return pd.Series([None, None, None, None], index=['min_within_quantile','max_within_quantile','min_model','max_model']) - - # # 初始化最小和最大值为None - # min_value = None - # max_value = None - # min_value_model = '' - # max_value_model = '' - - - # # 遍历指定列,找出在分位数范围内的最大最小值 - # for model in modelnames: - # value = row[model] - # if value >= q10 and value <= q90: - # if min_value is None or value < min_value: - # min_value = value - # min_value_model = model - - # if max_value is None or value > max_value: - # max_value = value - # max_value_model = model - - # # 返回最大最小值 - # return pd.Series([min_value, max_value,min_value_model,max_value_model], index=['min_within_quantile', 'max_within_quantile','min_model','max_model']) - - # # 应用函数到每一行 - # 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) - # # 保存到数据库 - # 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) - - ''' - # 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 - - - # 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) - df_predict2 = df_predict.copy() - 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 - 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,"testandpredict_groupby.csv"),index=False) - df_combined3['ds'] = df_combined3['ds'].dt.strftime('%Y-%m-%d 00:00:00') - # # 判断表存在 - if not sqlitedb.check_table_exists('testandpredict_groupby'): - df_combined3.to_sql('testandpredict_groupby',sqlitedb.connection,index=False) - else: - 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: - 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()) - - ten_models = allmodelnames - # 计算每个模型的方差 - variances = df_combined3[ten_models].var() - # 找到方差最大的模型 - max_variance_model = variances.idxmax() - # 打印方差最大的模型 - print("方差最大的模型是:", max_variance_model) - # 去掉方差最大的模型 - df_combined3 = df_combined3.drop(columns=[max_variance_model]) - if max_variance_model in allmodelnames: - allmodelnames.remove(max_variance_model) - df_combined3['min'] = df_combined3[allmodelnames].min(axis=1) - df_combined3['max'] = df_combined3[allmodelnames].max(axis=1) - print(df_combined3[['min','max']]) - # 历史价格+预测价格 - df_combined3 = df_combined3[-50:] # 取50个数据点画图 - plt.figure(figsize=(20, 10)) - plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值',marker='o') - plt.plot(df_combined3['ds'], df_combined3[most_model], label=most_model_name) - plt.fill_between(df_combined3['ds'], df_combined3['min'], df_combined3['max'], alpha=0.2) - plt.grid(True) - # # 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') - # 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() - ''' - # # 历史数据+预测数据 # # 拼接未来时间预测 df_predict = pd.read_csv(os.path.join(dataset,'predict.csv')) @@ -874,6 +586,36 @@ def model_losss_juxiting(sqlitedb): sqlitedb.insert_data('trueandpredict',tuple(row_dict.values()),columns=columns) first_row_to_database(df_predict) + + df_combined3 = pd.concat([df_combined3, df_predict]).reset_index(drop=True) + + # 计算每个模型与最佳模型的绝对误差比例,根据设置的阈值rote筛选预测值显示最大最小值 + names = [] + names_df = df_combined3.copy() + for col in allmodelnames: + names_df[f'{col}-{most_model_name}-误差比例'] = abs(names_df[col] - names_df[most_model_name]) / names_df[most_model_name] + names.append(f'{col}-{most_model_name}-误差比例') + + names_df = names_df[names] + def add_rote_column(row): + columns = [] + for r in names_df.columns: + if row[r] <= rote: + columns.append(r.split('-')[0]) + return pd.Series([columns], index=['columns']) + names_df['columns'] = names_df.apply(add_rote_column, axis=1) + + def add_upper_lower_bound(row): + print(row['columns']) + print(type(row['columns'])) + # 计算上边界值 + upper_bound = df_combined3.loc[row.name,row['columns']].max() + # 计算下边界值 + lower_bound = df_combined3.loc[row.name,row['columns']].min() + return pd.Series([lower_bound, upper_bound], index=['lower_bound', 'upper_bound']) + df_combined3[['upper_bound','lower_bound']] = names_df.apply(add_upper_lower_bound, axis=1) + + def find_most_common_model(): # 最多频率的模型名称 min_model_max_frequency_model = df_combined3['min_model'].tail(20).value_counts().idxmax() @@ -888,7 +630,7 @@ def model_losss_juxiting(sqlitedb): df_predict['max_within_quantile'] = df_predict[max_model_max_frequency_model] - find_most_common_model() + # find_most_common_model() df_predict2 = df_predict.copy() df_predict2['ds'] = pd.to_datetime(df_predict2['ds']) @@ -912,9 +654,8 @@ def model_losss_juxiting(sqlitedb): 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 - _add_abs_error_rate() + # _add_abs_error_rate() - 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: @@ -942,13 +683,13 @@ def model_losss_juxiting(sqlitedb): def _plt_predict_ture(df): - lens = df.shape[0] if df.shape[0] < 180 else 180 + lens = df.shape[0] if df.shape[0] < 180 else 90 df = df[-lens:] # 取180个数据点画图 # 历史价格 plt.figure(figsize=(20, 10)) plt.plot(df['ds'], df['y'], label='真实值') # 颜色填充 - plt.fill_between(df['ds'], df['min_within_quantile'], df['max_within_quantile'], alpha=0.2) + plt.fill_between(df['ds'], df['upper_bound'], df['lower_bound'], 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) @@ -1518,7 +1259,6 @@ def brent_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inpu except TimeoutError as e: print(f"请求超时: {e}") - def pp_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inputsize=5,dataset='dataset',time = '2024-07-30',reportname='report.pdf',sqlitedb='jbsh_yuanyou.db'): global y # 创建内容对应的空列表 @@ -1576,32 +1316,13 @@ def pp_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inputsi # except Exception as e: # print(f'绘制第{i+1}个特征{col}与价格散点图时出错:{e}') - - + ### 添加标题 content.append(Graphs.draw_title(f'{y}{time}预测报告')) - ### 预测结果 content.append(Graphs.draw_little_title('一、预测结果:')) # 添加历史走势及预测价格的走势图片 content.append(Graphs.draw_img(os.path.join(dataset,'历史价格-预测值.png'))) - # 根据真实值分组,去掉最高最小预测值画图逻辑 - content.append(Graphs.draw_text('图示说明:')) - content.append(Graphs.draw_text('1. 将所有模型的预测结果进行分组,大于真实值的为一组,小于真实值的为一组,去掉最高的预测值,去掉最小的预测值')) - content.append(Graphs.draw_text('2. 确定通道上界:在大于真实值的分组中,取最大的预测值')) - content.append(Graphs.draw_text('3. 确定通道下界:在小于真实值的分组中,取第二小的预测值')) - content.append(Graphs.draw_text('4. 预测结果没有真实值作为参考依据,通道上界取近20个交易日内预测在上界值的模型对应的预测值,通道下界同理;')) - content.append(Graphs.draw_text('5. 预测结果选用近20个交易日内,最多接近真实值的模型的预测值对应的预测结果;')) - content.append(Graphs.draw_text('6. 预测结果在通道外的,代表最接近真实值的预测结果不在置信波动范围内。')) - # 波动率画图逻辑 - # content.append(Graphs.draw_text('图示说明:')) - # content.append(Graphs.draw_text('1. 确定波动率置信区间:统计近60个交易日的真实价格波动率,找出在 10% ,90% 的分位值作为波动率置信区间;')) - # content.append(Graphs.draw_text('2. 确定通道上界:在所有模型的预测结果中 <= 前一天真实价格 乘以 90%的置信波动分位数')) - # content.append(Graphs.draw_text('3. 确定通道下界:在所有模型的预测结果中 >= 前一天真实价格 乘以 10%的置信波动分位数')) - # content.append(Graphs.draw_text('4. 预测结果没有真实值作为参考依据,通道上界取近20个交易日内预测在上界值的模型对应的预测值,通道下界同理;')) - # content.append(Graphs.draw_text('5. 预测结果选用近20个交易日内,最多接近真实值的模型的预测值对应的预测结果;')) - # content.append(Graphs.draw_text('6. 预测结果在通道外的,代表最接近真实值的预测结果不在置信波动范围内。')) - # 取df中y列为空的行 import pandas as pd @@ -1753,222 +1474,6 @@ def pp_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inputsi content.append(Graphs.draw_text('气泡图中,横轴为指标分类,纵轴为指标分类下的特征数量,气泡的面积越大表示该分类中特征的相关系数和越大。')) logger.info(f'绘制相关性总和的气泡图结束') - - - # # 计算特征相关性 - # data.rename(columns={y: 'y'}, inplace=True) - # data['ds'] = pd.to_datetime(data['ds']) - # data.drop(columns=['ds'], inplace=True) - # # 创建一个空的 DataFrame 来保存相关系数 - # correlation_df = pd.DataFrame(columns=['Feature', 'Correlation']) - # # 计算各特征与目标列的皮尔逊相关系数,并保存到新的 Data 中 - # for col in data.columns: - # if col!= 'y': - # pearson_correlation = np.corrcoef(data[col], data['y'])[0, 1] - # spearman_correlation, _ = spearmanr(data[col], data['y']) - # new_row = {'Feature': col, 'Pearson_Correlation': round(pearson_correlation,3), 'Spearman_Correlation': round(spearman_correlation,2)} - # correlation_df = correlation_df._append(new_row, ignore_index=True) - - # correlation_df.drop('Correlation', axis=1, inplace=True) - # correlation_df.dropna(inplace=True) - # correlation_df.to_csv(os.path.join(dataset,'指标相关性分析.csv'), index=False) - - # data = correlation_df['Pearson_Correlation'].values.tolist() - # # 生成 -1 到 1 的 20 个区间 - # bins = np.linspace(-1, 1, 21) - # # 计算每个区间的统计数(这里是区间内数据的数量) - # hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)] - - # #设置画布大小 - # plt.figure(figsize=(10, 6)) - # # 绘制直方图 - # plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0])) - - # # 添加标题和坐标轴标签 - # plt.title('皮尔逊相关系数分布图') - # plt.xlabel('区间') - # plt.ylabel('统计数') - # plt.savefig(os.path.join(dataset, '皮尔逊相关性系数.png')) - # plt.close() - - - # #设置画布大小 - # plt.figure(figsize=(10, 6)) - # data = correlation_df['Spearman_Correlation'].values.tolist() - # # 计算每个区间的统计数(这里是区间内数据的数量) - # hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)] - - # # 绘制直方图 - # plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0])) - - # # 添加标题和坐标轴标签 - # plt.title('斯皮尔曼相关系数分布图') - # plt.xlabel('区间') - # plt.ylabel('统计数') - # plt.savefig(os.path.join(dataset, '斯皮尔曼相关性系数.png')) - # plt.close() - # content.append(Graphs.draw_text(f'指标相关性分析--皮尔逊相关系数:')) - # # 皮尔逊正相关 不相关 负相关 的表格 - # content.append(Graphs.draw_img(os.path.join(dataset,'皮尔逊相关性系数.png'))) - # content.append(Graphs.draw_text('''皮尔逊相关系数说明:''')) - # content.append(Graphs.draw_text('''衡量两个特征之间的线性相关性。''')) - # content.append(Graphs.draw_text(''' - # 相关系数为1:表示两个变量之间存在完全正向的线性关系,即当一个变量增加时,另一个变量也相应增加,且变化是完全一致的。''')) - # content.append(Graphs.draw_text('''当前特征中正相关前十的有:''')) - # top10_columns = correlation_df.sort_values(by='Pearson_Correlation',ascending=False).head(10)['Feature'].to_list() - # top10 = ','.join(top10_columns) - # content.append(Graphs.draw_text(f'''{top10}''')) - - # feature_df = feature_data_df[['ds','y']+top10_columns] - # # 遍历X每一列,和yy画散点图 , - # for i, col in enumerate(feature_df.columns): - # print(f'正在绘制第{i+1}个特征{col}与价格散点图...') - # if col not in ['ds', 'y']: - # fig, ax1 = plt.subplots(figsize=(10, 6)) - # # 在第一个坐标轴上绘制数据 - # ax1.plot(feature_df['ds'], feature_df['y'], 'b-') - # ax1.set_xlabel('日期') - # ax1.set_ylabel('y', color='b') - # ax1.tick_params('y', colors='b') - # # 在 ax1 上添加文本显示值,添加一定的偏移避免值与曲线重叠 - # for j in range(1,len(feature_df),2): - # value = feature_df['y'].iloc[j] - # date = feature_df['ds'].iloc[j] - # offset = 1.001 - # ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10) - # # 创建第二个坐标轴 - # ax2 = ax1.twinx() - # # 在第二个坐标轴上绘制数据 - # line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-') - # ax2.set_ylabel(col, color='r') - # ax2.tick_params('y', colors='r') - # # 在 ax2 上添加文本显示值,添加一定的偏移避免值与曲线重叠 - # for j in range(0,len(feature_df),2): - # value = feature_df[col].iloc[j] - # date = feature_df['ds'].iloc[j] - # offset = 1.001 - # ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10) - # # 添加标题 - # plt.title(col) - # # 设置横坐标为日期格式并自动调整 - # locator = mdates.AutoDateLocator() - # formatter = mdates.AutoDateFormatter(locator) - # ax1.xaxis.set_major_locator(locator) - # ax1.xaxis.set_major_formatter(formatter) - # # 文件名特殊字符处理 - # col = col.replace('*', '-') - # col = col.replace(':', '-') - # plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png')) - # content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png'))) - # plt.close() - - - # content.append(Graphs.draw_text(f'指标相关性分析--斯皮尔曼相关系数:')) - # # 皮尔逊正相关 不相关 负相关 的表格 - # content.append(Graphs.draw_img(os.path.join(dataset,'斯皮尔曼相关性系数.png'))) - # content.append(Graphs.draw_text('斯皮尔曼相关系数(Spearmans rank correlation coefficient)是一种用于衡量两个变量之间的单调关系(不一定是线性关系)的统计指标。')) - # content.append(Graphs.draw_text('它的计算基于变量的秩次(即变量值的排序位置)而非变量的原始值。')) - # content.append(Graphs.draw_text('斯皮尔曼相关系数的取值范围在 -1 到 1 之间。')) - # content.append(Graphs.draw_text('当系数为 1 时,表示两个变量之间存在完全正的单调关系;')) - # content.append(Graphs.draw_text('''当前特征中正单调关系前十的有:''')) - # top10_columns = correlation_df.sort_values(by='Spearman_Correlation',ascending=False).head(10)['Feature'].to_list() - # top10 = ','.join(top10_columns) - # content.append(Graphs.draw_text(f'''{top10}''')) - - # feature_df = feature_data_df[['ds','y']+top10_columns] - # # 遍历X每一列,和yy画散点图 , - # for i, col in enumerate(feature_df.columns): - # print(f'正在绘制第{i+1}个特征{col}与价格散点图...') - # if col not in ['ds', 'y']: - # fig, ax1 = plt.subplots(figsize=(10, 6)) - # # 在第一个坐标轴上绘制数据 - # ax1.plot(feature_df['ds'], feature_df['y'], 'b-') - # ax1.set_xlabel('日期') - # ax1.set_ylabel('y', color='b') - # ax1.tick_params('y', colors='b') - # # 在 ax1 上添加文本显示值,添加一定的偏移避免值与曲线重叠 - # for j in range(1,len(feature_df),2): - # value = feature_df['y'].iloc[j] - # date = feature_df['ds'].iloc[j] - # offset = 1.001 - # ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10) - # # 创建第二个坐标轴 - # ax2 = ax1.twinx() - # # 在第二个坐标轴上绘制数据 - # line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-') - # ax2.set_ylabel(col, color='r') - # ax2.tick_params('y', colors='r') - # # 在 ax2 上添加文本显示值,添加一定的偏移避免值与曲线重叠 - # for j in range(0,len(feature_df),2): - # value = feature_df[col].iloc[j] - # date = feature_df['ds'].iloc[j] - # offset = 1.001 - # ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10) - # # 添加标题 - # plt.title(col) - # # 设置横坐标为日期格式并自动调整 - # locator = mdates.AutoDateLocator() - # formatter = mdates.AutoDateFormatter(locator) - # ax1.xaxis.set_major_locator(locator) - # ax1.xaxis.set_major_formatter(formatter) - # # 文件名特殊字符处理 - # col = col.replace('*', '-') - # col = col.replace(':', '-') - # plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png')) - # content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png'))) - # plt.close() - - # content.append(Graphs.draw_text('当系数为 -1 时,表示存在完全负的单调关系;')) - # content.append(Graphs.draw_text('''当前特征中负单调关系前十的有:''')) - # tail10_columns = correlation_df.sort_values(by='Spearman_Correlation',ascending=True).head(10)['Feature'].to_list() - # top10 = ','.join(tail10_columns) - # content.append(Graphs.draw_text(f'''{top10}''')) - # # 获取特征的近一周值 - # feature_df = feature_data_df[['ds','y']+tail10_columns] - # # 遍历X每一列,和yy画散点图 , - # for i, col in enumerate(feature_df.columns): - # print(f'正在绘制第{i+1}个特征{col}与价格散点图...') - # if col not in ['ds', 'y']: - # fig, ax1 = plt.subplots(figsize=(10, 6)) - # # 在第一个坐标轴上绘制数据 - # ax1.plot(feature_df['ds'], feature_df['y'], 'b-') - # ax1.set_xlabel('日期') - # ax1.set_ylabel('y', color='b') - # ax1.tick_params('y', colors='b') - # # 在 ax1 上添加文本显示值,添加一定的偏移避免值与曲线重叠 - # for j in range(len(feature_df)): - # if j%2 == 1: - # value = feature_df['y'].iloc[j] - # date = feature_df['ds'].iloc[j] - # offset = 1.001 - # ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10) - # # 创建第二个坐标轴 - # ax2 = ax1.twinx() - # # 在第二个坐标轴上绘制数据 - # line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-') - # ax2.set_ylabel(col, color='r') - # ax2.tick_params('y', colors='r') - # # 在 ax2 上添加文本显示值,添加一定的偏移避免值与曲线重叠 - # for j in range(1,len(feature_df),2): - # value = feature_df[col].iloc[j] - # date = feature_df['ds'].iloc[j] - # offset = 1.001 - # ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10) - # # 添加标题 - # plt.title(col) - # # 设置横坐标为日期格式并自动调整 - # locator = mdates.AutoDateLocator() - # formatter = mdates.AutoDateFormatter(locator) - # ax1.xaxis.set_major_locator(locator) - # ax1.xaxis.set_major_formatter(formatter) - # # 文件名特殊字符处理 - # col = col.replace('*', '-') - # col = col.replace(':', '-') - # plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png')) - # content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png'))) - # plt.close() - # content.append(Graphs.draw_text('当系数为 0 时,表示两个变量之间不存在单调关系。')) - # content.append(Graphs.draw_text('与皮尔逊相关系数相比,斯皮尔曼相关系数对于数据中的异常值不敏感,更适用于处理非线性关系或存在极端值的数据。')) content.append(Graphs.draw_little_title('模型选择:')) content.append(Graphs.draw_text(f'预测使用了{num_models}个模型进行训练拟合,通过评估指标MAE从小到大排列,前5个模型的简介如下:')) @@ -1990,12 +1495,9 @@ def pp_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inputsi eval_df[col] = eval_df[col].round(3) # 筛选 fivemodels_list.tolist() 的行 eval_df = eval_df[eval_df['模型(Model)'].isin(fivemodels_list)] - # df转置 eval_df = eval_df.T - # df重置索引 eval_df = eval_df.reset_index() eval_df = eval_df.T - # # 添加表格 data = eval_df.values.tolist() col_width = 500/len(eval_df.columns) content.append(Graphs.draw_table(col_width,*data)) @@ -2004,7 +1506,6 @@ def pp_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inputsi content.append(Graphs.draw_text('2. 平均绝对误差(MAE):平均绝对误差是衡量预测值与实际值之间误差的一种方法,取值越小,误差越小,预测效果越好。')) content.append(Graphs.draw_text('3. 平均平方误差(MSE):平均平方误差是衡量预测值与实际值之间误差的一种方法,取值越小,误差越小,预测效果越好。')) content.append(Graphs.draw_text('模型拟合:')) - # 添加图片 content.append(Graphs.draw_img(os.path.join(dataset,'预测值与真实值对比图.png'))) # 附1,特征列表 @@ -2017,14 +1518,11 @@ def pp_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inputsi for i in range(len(fuyi)): content.append(Graphs.draw_text(f'{i+1}、{fuyi[i]}')) - - ### 生成pdf文件 doc = SimpleDocTemplate(os.path.join(dataset,reportname), pagesize=letter) # doc = SimpleDocTemplate(os.path.join(dataset,'reportname.pdf'), pagesize=letter) doc.build(content) # pdf 上传到数字化信息平台 - # 读取pdf并转为base64 try: if is_update_report: with open(os.path.join(dataset,reportname), 'rb') as f: @@ -2036,8 +1534,6 @@ def pp_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inputsi except TimeoutError as e: print(f"请求超时: {e}") - - def pp_export_pdf_v1(num_indicators=475,num_models=21, num_dayindicator=202,inputsize=5,dataset='dataset',time = '2024-07-30',reportname='report.pdf'): global y # 创建内容对应的空列表 @@ -2374,9 +1870,6 @@ def pp_export_pdf_v1(num_indicators=475,num_models=21, num_dayindicator=202,inpu except TimeoutError as e: print(f"请求超时: {e}") - - - def tansuanli_export_pdf(num_indicators=475,num_models=22, num_dayindicator=202,inputsize=5,dataset='dataset',y='电碳价格',end_time='2024-07-30',reportname='tansuanli.pdf'): # 创建内容对应的空列表 content = list() diff --git a/yuanyoudataset/jbsh_yuanyou.db b/yuanyoudataset/jbsh_yuanyou.db index bd0953a..eb31dc5 100644 Binary files a/yuanyoudataset/jbsh_yuanyou.db and b/yuanyoudataset/jbsh_yuanyou.db differ