同步代码
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codeback.py
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494
codeback.py
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################################### 报告内容 ##################################
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# 根据真实值分组，去掉最高最小预测值画图逻辑
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# content.append(Graphs.draw_text('图示说明：'))
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# 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'))
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# 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'))
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# 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'))
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# 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；'))
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# 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；'))
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# 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。'))
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# 波动率画图逻辑
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# content.append(Graphs.draw_text('图示说明：'))
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# 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;'))
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# 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'))
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# 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'))
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# 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；'))
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# 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；'))
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# 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。'))
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# # 计算特征相关性
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# data.rename(columns={y: 'y'}, inplace=True)
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# data['ds'] = pd.to_datetime(data['ds'])
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# data.drop(columns=['ds'], inplace=True)
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# # 创建一个空的 DataFrame 来保存相关系数
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# correlation_df = pd.DataFrame(columns=['Feature', 'Correlation'])
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# # 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
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# for col in data.columns:
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# if col!= 'y':
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# pearson_correlation = np.corrcoef(data[col], data['y'])[0, 1]
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# spearman_correlation, _ = spearmanr(data[col], data['y'])
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# new_row = {'Feature': col, 'Pearson_Correlation': round(pearson_correlation,3), 'Spearman_Correlation': round(spearman_correlation,2)}
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# correlation_df = correlation_df._append(new_row, ignore_index=True)
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# correlation_df.drop('Correlation', axis=1, inplace=True)
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# correlation_df.dropna(inplace=True)
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# correlation_df.to_csv(os.path.join(dataset,'指标相关性分析.csv'), index=False)
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# data = correlation_df['Pearson_Correlation'].values.tolist()
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# # 生成 -1 到 1 的 20 个区间
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# bins = np.linspace(-1, 1, 21)
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# # 计算每个区间的统计数（这里是区间内数据的数量）
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# hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)]
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# #设置画布大小
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# plt.figure(figsize=(10, 6))
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# # 绘制直方图
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# plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0]))
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# # 添加标题和坐标轴标签
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# plt.title('皮尔逊相关系数分布图')
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# plt.xlabel('区间')
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# plt.ylabel('统计数')
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# plt.savefig(os.path.join(dataset, '皮尔逊相关性系数.png'))
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# plt.close()
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# #设置画布大小
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# plt.figure(figsize=(10, 6))
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# data = correlation_df['Spearman_Correlation'].values.tolist()
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# # 计算每个区间的统计数（这里是区间内数据的数量）
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# hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)]
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# # 绘制直方图
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# plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0]))
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# # 添加标题和坐标轴标签
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# plt.title('斯皮尔曼相关系数分布图')
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# plt.xlabel('区间')
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# plt.ylabel('统计数')
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# plt.savefig(os.path.join(dataset, '斯皮尔曼相关性系数.png'))
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# plt.close()
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# content.append(Graphs.draw_text(f'指标相关性分析--皮尔逊相关系数：'))
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# # 皮尔逊正相关 不相关 负相关 的表格
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# content.append(Graphs.draw_img(os.path.join(dataset,'皮尔逊相关性系数.png')))
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# content.append(Graphs.draw_text('''皮尔逊相关系数说明：'''))
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# content.append(Graphs.draw_text('''衡量两个特征之间的线性相关性。'''))
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# content.append(Graphs.draw_text('''
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# 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。'''))
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# content.append(Graphs.draw_text('''当前特征中正相关前十的有：'''))
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# top10_columns = correlation_df.sort_values(by='Pearson_Correlation',ascending=False).head(10)['Feature'].to_list()
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# top10 = ','.join(top10_columns)
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# content.append(Graphs.draw_text(f'''{top10}'''))
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# feature_df = feature_data_df[['ds','y']+top10_columns]
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# # 遍历X每一列，和yy画散点图 ，
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# for i, col in enumerate(feature_df.columns):
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# print(f'正在绘制第{i+1}个特征{col}与价格散点图...')
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# if col not in ['ds', 'y']:
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# fig, ax1 = plt.subplots(figsize=(10, 6))
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# # 在第一个坐标轴上绘制数据
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# ax1.plot(feature_df['ds'], feature_df['y'], 'b-')
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# ax1.set_xlabel('日期')
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# ax1.set_ylabel('y', color='b')
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# ax1.tick_params('y', colors='b')
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# # 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
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# for j in range(1,len(feature_df),2):
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# value = feature_df['y'].iloc[j]
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# date = feature_df['ds'].iloc[j]
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# offset = 1.001
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# ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10)
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# # 创建第二个坐标轴
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# ax2 = ax1.twinx()
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# # 在第二个坐标轴上绘制数据
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# line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-')
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# ax2.set_ylabel(col, color='r')
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# ax2.tick_params('y', colors='r')
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# # 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
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# for j in range(0,len(feature_df),2):
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# value = feature_df[col].iloc[j]
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# date = feature_df['ds'].iloc[j]
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# offset = 1.001
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# ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10)
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# # 添加标题
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# plt.title(col)
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# # 设置横坐标为日期格式并自动调整
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# locator = mdates.AutoDateLocator()
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# formatter = mdates.AutoDateFormatter(locator)
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# ax1.xaxis.set_major_locator(locator)
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# ax1.xaxis.set_major_formatter(formatter)
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# # 文件名特殊字符处理
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# col = col.replace('*', '-')
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# col = col.replace(':', '-')
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# plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png'))
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# content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png')))
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# plt.close()
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# content.append(Graphs.draw_text(f'指标相关性分析--斯皮尔曼相关系数：'))
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# # 皮尔逊正相关 不相关 负相关 的表格
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# content.append(Graphs.draw_img(os.path.join(dataset,'斯皮尔曼相关性系数.png')))
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# 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。'))
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# 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。'))
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# content.append(Graphs.draw_text('斯皮尔曼相关系数的取值范围在 -1 到 1 之间。'))
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# 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；'))
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# content.append(Graphs.draw_text('''当前特征中正单调关系前十的有：'''))
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# top10_columns = correlation_df.sort_values(by='Spearman_Correlation',ascending=False).head(10)['Feature'].to_list()
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# top10 = ','.join(top10_columns)
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# content.append(Graphs.draw_text(f'''{top10}'''))
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# feature_df = feature_data_df[['ds','y']+top10_columns]
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# # 遍历X每一列，和yy画散点图 ，
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# for i, col in enumerate(feature_df.columns):
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# print(f'正在绘制第{i+1}个特征{col}与价格散点图...')
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# if col not in ['ds', 'y']:
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# fig, ax1 = plt.subplots(figsize=(10, 6))
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# # 在第一个坐标轴上绘制数据
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# ax1.plot(feature_df['ds'], feature_df['y'], 'b-')
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# ax1.set_xlabel('日期')
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# ax1.set_ylabel('y', color='b')
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# ax1.tick_params('y', colors='b')
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# # 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
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# for j in range(1,len(feature_df),2):
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# value = feature_df['y'].iloc[j]
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# date = feature_df['ds'].iloc[j]
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# offset = 1.001
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# ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10)
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# # 创建第二个坐标轴
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# ax2 = ax1.twinx()
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# # 在第二个坐标轴上绘制数据
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# line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-')
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# ax2.set_ylabel(col, color='r')
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# ax2.tick_params('y', colors='r')
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# # 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
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# for j in range(0,len(feature_df),2):
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# value = feature_df[col].iloc[j]
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# date = feature_df['ds'].iloc[j]
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# offset = 1.001
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# ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10)
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# # 添加标题
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# plt.title(col)
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# # 设置横坐标为日期格式并自动调整
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# locator = mdates.AutoDateLocator()
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# formatter = mdates.AutoDateFormatter(locator)
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# ax1.xaxis.set_major_locator(locator)
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# ax1.xaxis.set_major_formatter(formatter)
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# # 文件名特殊字符处理
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# col = col.replace('*', '-')
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# col = col.replace(':', '-')
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# plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png'))
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# content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png')))
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# plt.close()
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# content.append(Graphs.draw_text('当系数为 -1 时，表示存在完全负的单调关系；'))
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# content.append(Graphs.draw_text('''当前特征中负单调关系前十的有：'''))
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# tail10_columns = correlation_df.sort_values(by='Spearman_Correlation',ascending=True).head(10)['Feature'].to_list()
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# top10 = ','.join(tail10_columns)
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# content.append(Graphs.draw_text(f'''{top10}'''))
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# # 获取特征的近一周值
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# feature_df = feature_data_df[['ds','y']+tail10_columns]
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# # 遍历X每一列，和yy画散点图 ，
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# for i, col in enumerate(feature_df.columns):
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# print(f'正在绘制第{i+1}个特征{col}与价格散点图...')
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# if col not in ['ds', 'y']:
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# fig, ax1 = plt.subplots(figsize=(10, 6))
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# # 在第一个坐标轴上绘制数据
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# ax1.plot(feature_df['ds'], feature_df['y'], 'b-')
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# ax1.set_xlabel('日期')
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# ax1.set_ylabel('y', color='b')
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# ax1.tick_params('y', colors='b')
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# # 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
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# for j in range(len(feature_df)):
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# if j%2 == 1:
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# value = feature_df['y'].iloc[j]
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# date = feature_df['ds'].iloc[j]
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# offset = 1.001
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# ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10)
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# # 创建第二个坐标轴
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# ax2 = ax1.twinx()
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# # 在第二个坐标轴上绘制数据
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# line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-')
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# ax2.set_ylabel(col, color='r')
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# ax2.tick_params('y', colors='r')
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# # 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
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# for j in range(1,len(feature_df),2):
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# value = feature_df[col].iloc[j]
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# date = feature_df['ds'].iloc[j]
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# offset = 1.001
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# ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10)
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# # 添加标题
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# plt.title(col)
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# # 设置横坐标为日期格式并自动调整
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# locator = mdates.AutoDateLocator()
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# formatter = mdates.AutoDateFormatter(locator)
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# ax1.xaxis.set_major_locator(locator)
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# ax1.xaxis.set_major_formatter(formatter)
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# # 文件名特殊字符处理
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# col = col.replace('*', '-')
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# col = col.replace(':', '-')
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# plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png'))
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# content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png')))
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# plt.close()
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# 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。'))
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# 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。'))
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################################### 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()
|
||||||
@ -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
|
### 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
|
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
|
### 文件
|
||||||
data_set = 'PP指标数据.xlsx' # 数据集文件
|
data_set = 'PP指标数据.xlsx' # 数据集文件
|
||||||
|
|||||||
Binary file not shown.
File diff suppressed because it is too large
Load Diff
@ -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
|
ds,NHITS,Informer,LSTM,iTransformer,TSMixer,TSMixerx,PatchTST,RNN,GRU,TCN,BiTCN,DilatedRNN,MLP,DLinear,NLinear,TFT,StemGNN,MLPMultivariate,TiDE,DeepNPTS,y,upper_bound,lower_bound
|
||||||
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
|
2024-01-30,7314.25,7328.16,7255.32,7385.89,7296.98,7304.24,7288.53,7316.8,7357.77,7406.36,7330.33,7284.31,7336.16,7282.57,7305.31,7289.84,7293.28,7325.51,7297.12,7304.88,7315.0,7255.32,7406.36
|
||||||
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
|
2024-01-31,7313.85,7344.02,7260.27,7412.49,7306.93,7303.91,7291.97,7327.11,7364.25,7409.82,7340.24,7298.74,7321.85,7294.2,7298.47,7302.1,7291.73,7313.51,7302.49,7313.87,7302.5,7260.27,7412.49
|
||||||
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
|
2024-02-01,7297.0,7335.94,7250.98,7416.15,7298.8,7304.56,7284.13,7314.06,7351.66,7406.99,7334.87,7290.45,7319.52,7291.02,7287.84,7290.86,7290.56,7310.52,7301.71,7297.39,7275.0,7250.98,7416.15
|
||||||
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
|
2024-02-02,7275.51,7311.56,7233.06,7384.25,7272.41,7282.94,7263.86,7294.73,7330.68,7395.3,7306.83,7260.65,7289.64,7278.71,7262.57,7266.32,7287.96,7292.17,7280.73,7271.37,7242.5,7233.06,7395.3
|
||||||
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
|
2024-02-04,7236.79,7275.39,7212.67,7293.53,7239.09,7232.82,7228.47,7267.82,7308.6,7378.92,7272.09,7228.76,7248.22,7249.75,7245.09,7227.47,7283.85,7261.78,7249.74,7287.39,7240.0,7212.67,7378.92
|
||||||
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
|
2024-02-05,7237.9,7251.93,7211.69,7252.2,7225.67,7197.03,7226.16,7266.29,7310.45,7374.66,7241.46,7223.16,7238.12,7228.83,7231.89,7224.25,7279.5,7252.87,7230.67,7307.89,7227.5,7197.03,7374.66
|
||||||
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
|
2024-02-06,7224.19,7245.36,7203.25,7219.99,7215.03,7174.85,7206.81,7257.77,7300.49,7369.7,7214.19,7210.87,7217.43,7212.0,7214.21,7214.99,7274.58,7235.42,7215.9,7300.92,7246.67,7174.85,7369.7
|
||||||
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
|
2024-02-07,7237.08,7248.68,7216.83,7236.0,7226.42,7222.58,7234.44,7277.5,7317.63,7377.89,7198.21,7229.34,7228.86,7227.31,7229.82,7221.54,7270.66,7244.52,7230.4,7282.88,7255.0,7198.21,7377.89
|
||||||
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
|
2024-02-08,7249.03,7235.45,7221.61,7240.36,7242.4,7235.89,7252.49,7285.7,7321.4,7382.84,7189.06,7239.06,7239.93,7231.63,7251.7,7236.11,7267.21,7248.82,7252.08,7254.66,7270.0,7189.06,7382.84
|
||||||
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
|
2024-02-18,7257.58,7237.42,7232.76,7222.87,7258.08,7252.65,7269.86,7295.25,7334.03,7388.26,7192.12,7253.86,7251.03,7230.31,7268.39,7263.02,7264.68,7252.85,7272.44,7244.53,7336.67,7192.12,7388.26
|
||||||
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
|
2024-02-19,7329.8,7284.3,7279.87,7267.22,7312.25,7328.12,7333.28,7359.55,7391.84,7412.85,7285.8,7321.05,7295.19,7280.82,7344.06,7349.32,7263.9,7278.66,7323.31,7273.12,7350.0,7263.9,7412.85
|
||||||
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
|
2024-02-20,7343.01,7300.71,7287.02,7292.98,7338.11,7398.41,7344.57,7358.8,7393.28,7424.57,7353.24,7341.83,7322.19,7304.78,7354.74,7371.01,7263.52,7308.91,7334.03,7338.48,7335.0,7263.52,7424.57
|
||||||
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
|
2024-02-21,7331.02,7293.98,7277.52,7299.13,7334.13,7417.16,7333.0,7344.13,7379.12,7429.64,7381.5,7344.5,7320.66,7332.96,7337.17,7360.45,7262.8,7310.89,7325.44,7337.84,7297.5,7262.8,7429.64
|
||||||
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
|
2024-02-22,7286.55,7268.48,7249.85,7303.54,7304.14,7392.3,7305.8,7314.63,7345.37,7420.07,7373.5,7311.53,7302.57,7318.15,7298.16,7284.19,7261.08,7290.86,7292.32,7342.6,7285.0,7249.85,7420.07
|
||||||
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
|
2024-02-23,7272.61,7257.99,7241.55,7334.59,7288.24,7365.99,7296.96,7295.77,7340.09,7413.29,7359.52,7296.67,7292.49,7303.51,7281.81,7269.99,7259.09,7278.62,7281.0,7323.98,7306.67,7241.55,7413.29
|
||||||
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
|
2024-02-26,7318.19,7259.8,7254.44,7305.31,7299.03,7362.35,7321.52,7317.68,7358.98,7413.28,7357.65,7301.95,7320.65,7296.1,7301.41,7307.13,7258.12,7293.71,7295.1,7371.11,7292.5,7254.44,7413.28
|
||||||
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
|
2024-02-27,7324.57,7256.94,7242.58,7270.35,7297.98,7317.99,7297.46,7304.28,7342.81,7408.67,7341.05,7289.78,7327.02,7292.81,7282.36,7282.91,7257.25,7299.94,7286.47,7368.65,7306.67,7242.58,7408.67
|
||||||
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
|
2024-02-28,7352.75,7296.76,7254.83,7288.43,7302.82,7348.5,7323.02,7324.8,7359.94,7413.98,7330.8,7305.92,7341.13,7310.9,7305.61,7301.83,7257.06,7317.34,7303.91,7380.02,7353.33,7254.83,7413.98
|
||||||
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
|
2024-02-29,7365.17,7316.5,7288.96,7312.89,7334.31,7378.47,7368.02,7371.75,7401.12,7425.51,7341.98,7336.51,7371.22,7333.33,7360.72,7354.87,7257.55,7350.62,7343.33,7374.52,7325.0,7257.55,7425.51
|
||||||
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
|
2024-03-01,7320.5,7310.36,7263.73,7287.41,7322.0,7343.2,7329.76,7327.44,7364.73,7416.31,7322.7,7306.52,7337.22,7315.45,7335.42,7314.74,7258.01,7320.63,7327.13,7294.14,7343.33,7258.01,7416.31
|
||||||
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
|
2024-03-04,7334.56,7330.49,7283.3,7283.08,7330.63,7344.31,7334.27,7357.22,7391.92,7422.3,7315.57,7328.23,7328.13,7333.05,7339.2,7314.98,7258.64,7317.71,7342.18,7296.81,7330.0,7258.64,7422.3
|
||||||
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
|
2024-03-05,7309.45,7323.87,7267.88,7272.86,7326.97,7335.79,7319.16,7340.15,7370.5,7418.13,7305.32,7318.76,7320.53,7326.05,7321.28,7293.51,7258.86,7305.56,7328.04,7281.27,7310.0,7258.86,7418.13
|
||||||
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
|
2024-03-06,7304.78,7322.92,7255.75,7327.1,7305.71,7311.24,7313.83,7311.53,7357.06,7412.45,7301.91,7304.3,7315.76,7309.79,7297.15,7279.6,7258.59,7297.71,7301.35,7288.95,7297.5,7255.75,7412.45
|
||||||
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
|
2024-03-07,7302.21,7319.55,7246.71,7319.4,7294.17,7309.87,7308.66,7314.28,7347.75,7407.47,7294.43,7290.02,7319.87,7285.87,7297.02,7274.12,7258.13,7292.83,7293.32,7295.53,7290.0,7246.71,7407.47
|
||||||
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
|
2024-03-08,7292.95,7295.46,7241.47,7278.36,7288.79,7302.01,7299.54,7302.63,7343.0,7403.52,7279.55,7288.72,7290.22,7284.58,7288.43,7271.91,7257.76,7280.84,7290.29,7294.8,7295.0,7241.47,7403.52
|
||||||
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
|
2024-03-11,7283.5,7305.23,7245.06,7251.93,7289.62,7272.81,7302.29,7308.69,7348.58,7402.53,7275.18,7290.97,7265.94,7297.8,7293.46,7274.37,7257.81,7273.62,7289.43,7294.81,7285.0,7245.06,7402.53
|
||||||
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
|
2024-03-12,7264.78,7296.75,7238.1,7243.38,7277.58,7269.54,7282.67,7303.06,7339.16,7395.51,7277.17,7280.3,7244.14,7296.69,7275.29,7269.44,7259.15,7263.53,7270.28,7285.48,7283.33,7238.1,7395.51
|
||||||
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
|
2024-03-13,7256.37,7290.6,7238.44,7256.14,7272.1,7275.87,7280.45,7301.9,7340.35,7398.0,7269.78,7285.73,7238.05,7303.68,7278.15,7276.54,7260.81,7262.14,7272.17,7252.64,7285.0,7238.05,7398.0
|
||||||
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
|
2024-03-14,7269.4,7278.73,7239.51,7271.19,7275.51,7277.1,7283.45,7304.5,7341.46,7397.56,7280.7,7284.09,7251.88,7273.88,7288.42,7290.29,7264.7,7271.5,7281.04,7264.11,7277.5,7239.51,7397.56
|
||||||
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
|
2024-03-15,7264.23,7278.01,7234.82,7271.32,7271.08,7262.54,7274.31,7295.47,7335.31,7395.98,7277.67,7279.69,7256.92,7252.44,7269.11,7283.47,7267.48,7272.5,7279.45,7273.04,7353.33,7234.82,7395.98
|
||||||
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
|
2024-03-18,7339.36,7326.0,7292.13,7296.54,7320.05,7302.48,7366.6,7371.87,7407.54,7422.89,7304.89,7348.6,7314.84,7304.7,7353.23,7347.31,7270.29,7316.5,7330.94,7344.93,7387.5,7270.29,7422.89
|
||||||
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
|
2024-03-19,7388.08,7383.64,7320.49,7322.57,7361.99,7336.57,7386.98,7397.01,7427.54,7439.6,7310.88,7378.46,7361.53,7326.46,7392.8,7379.9,7271.59,7364.34,7363.5,7387.24,7435.0,7271.59,7439.6
|
||||||
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
|
2024-03-20,7446.17,7402.18,7390.64,7384.41,7404.71,7354.35,7446.15,7449.59,7481.41,7465.93,7324.66,7439.02,7414.86,7384.65,7440.51,7410.61,7272.48,7422.26,7415.17,7432.14,7460.0,7272.48,7481.41
|
||||||
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
|
2024-03-21,7471.48,7404.45,7418.15,7440.61,7436.82,7382.55,7467.97,7485.88,7497.73,7485.05,7342.42,7469.99,7437.01,7433.13,7466.88,7407.96,7274.62,7469.2,7449.82,7435.73,7422.5,7274.62,7497.73
|
||||||
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
|
2024-03-22,7417.1,7385.08,7359.75,7498.68,7419.15,7377.38,7398.66,7419.29,7445.15,7473.64,7348.07,7429.75,7410.0,7426.43,7427.33,7392.96,7277.87,7427.39,7431.13,7377.32,7402.5,7277.87,7498.68
|
||||||
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
|
2024-03-25,7404.65,7346.23,7337.62,7516.85,7391.76,7372.08,7396.19,7403.69,7432.8,7463.86,7341.83,7408.89,7382.35,7426.01,7402.0,7391.26,7279.41,7377.64,7401.38,7352.68,7372.5,7279.41,7516.85
|
||||||
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
|
2024-03-26,7367.22,7320.87,7297.47,7417.68,7362.82,7379.8,7348.91,7365.84,7399.45,7449.14,7346.87,7376.77,7355.65,7392.1,7355.2,7356.75,7280.36,7337.61,7355.82,7332.97,7365.0,7280.36,7449.14
|
||||||
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
|
2024-03-27,7348.99,7317.14,7292.01,7388.13,7348.72,7361.01,7349.25,7354.78,7399.57,7441.3,7353.46,7370.99,7335.98,7369.87,7345.71,7338.27,7281.29,7317.34,7351.88,7327.51,7345.0,7281.29,7441.3
|
||||||
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
|
2024-03-28,7315.95,7297.52,7273.48,7369.49,7330.9,7328.15,7333.3,7346.44,7379.1,7428.09,7353.34,7339.56,7312.39,7352.16,7328.61,7330.9,7282.04,7293.26,7333.7,7319.06,7345.0,7273.48,7428.09
|
||||||
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
|
2024-03-29,7330.24,7310.89,7275.81,7335.7,7325.45,7312.22,7328.93,7348.29,7385.13,7421.9,7338.85,7331.57,7296.93,7335.55,7336.98,7327.3,7283.21,7280.78,7332.74,7339.16,7345.0,7275.81,7421.9
|
||||||
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
|
2024-04-01,7338.24,7313.88,7275.35,7289.31,7326.11,7304.88,7330.88,7350.42,7384.03,7416.0,7329.62,7326.53,7302.67,7334.68,7344.62,7326.0,7284.14,7295.26,7335.23,7344.61,7360.0,7275.35,7416.0
|
||||||
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
|
2024-04-02,7346.74,7336.2,7289.21,7260.13,7336.79,7317.73,7346.57,7364.25,7401.18,7422.32,7325.93,7337.44,7312.67,7330.29,7355.13,7331.72,7285.02,7297.27,7342.82,7353.49,7380.0,7260.13,7422.32
|
||||||
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
|
2024-04-03,7362.03,7370.42,7308.14,7328.17,7353.97,7345.56,7371.53,7386.28,7419.33,7428.12,7335.51,7363.3,7336.41,7329.53,7376.12,7363.62,7286.48,7306.97,7357.16,7375.72,7397.5,7286.48,7428.12
|
||||||
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
|
2024-04-07,7380.46,7408.82,7329.28,7356.73,7377.67,7353.99,7410.47,7402.89,7435.25,7440.61,7349.12,7386.35,7366.33,7351.98,7390.83,7357.89,7289.09,7339.57,7388.62,7390.25,7437.5,7289.09,7440.61
|
||||||
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
|
2024-04-08,7447.65,7444.44,7385.22,7378.92,7413.36,7379.34,7464.31,7450.51,7478.47,7458.92,7355.35,7434.99,7413.54,7377.16,7434.57,7405.02,7293.27,7389.93,7431.65,7422.17,7490.0,7293.27,7478.47
|
||||||
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
|
2024-04-09,7508.91,7436.74,7454.91,7440.4,7458.67,7404.45,7504.04,7508.22,7535.45,7498.07,7361.93,7500.56,7456.07,7436.46,7490.4,7479.82,7297.24,7454.98,7473.05,7482.98,7507.5,7297.24,7535.45
|
||||||
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
|
2024-04-10,7525.01,7462.49,7471.0,7563.84,7480.64,7407.01,7494.6,7518.41,7543.95,7529.9,7380.05,7529.39,7474.85,7472.81,7508.65,7484.08,7302.13,7479.24,7487.67,7500.6,7497.5,7302.13,7563.84
|
||||||
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
|
2024-04-11,7504.87,7449.02,7455.34,7652.17,7475.08,7402.14,7456.31,7507.48,7525.09,7529.47,7398.26,7516.28,7430.02,7497.04,7500.72,7442.53,7306.74,7443.74,7485.56,7459.59,7497.5,7306.74,7652.17
|
||||||
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
|
2024-04-12,7479.46,7435.64,7455.09,7572.84,7465.52,7413.95,7465.76,7506.2,7527.13,7527.37,7413.21,7518.23,7412.17,7521.26,7487.64,7408.6,7311.37,7418.86,7477.26,7416.74,7506.67,7311.37,7572.84
|
||||||
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
|
2024-04-15,7472.32,7396.84,7463.68,7542.39,7464.76,7388.03,7470.02,7506.19,7537.05,7531.6,7428.17,7524.9,7414.0,7503.56,7498.12,7387.92,7315.59,7405.19,7479.79,7412.79,7482.5,7315.59,7542.39
|
||||||
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
|
2024-04-16,7445.28,7382.92,7424.47,7382.77,7448.83,7379.28,7438.86,7474.01,7498.57,7514.49,7421.3,7486.23,7387.0,7473.29,7463.03,7381.95,7317.64,7276.64,7465.68,7344.09,7495.0,7276.64,7514.49
|
||||||
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
|
2024-04-17,7446.2,7386.32,7448.03,7333.2,7454.08,7369.86,7440.14,7500.86,7526.87,7516.48,7420.72,7502.13,7377.72,7467.53,7480.13,7386.93,7319.43,7251.79,7470.71,7356.98,7497.5,7251.79,7526.87
|
||||||
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
|
2024-04-18,7457.83,7387.47,7444.84,7268.26,7460.04,7373.79,7419.24,7500.61,7521.25,7508.95,7418.33,7495.19,7396.12,7451.62,7494.75,7389.4,7321.87,7247.1,7474.29,7391.17,7497.5,7247.1,7521.25
|
||||||
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
|
2024-04-19,7469.48,7401.84,7446.51,7248.99,7463.59,7377.7,7399.12,7494.52,7524.0,7505.1,7415.93,7489.72,7404.43,7461.7,7489.8,7391.95,7325.94,7224.31,7469.89,7418.57,7527.5,7224.31,7524.0
|
||||||
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
|
2024-04-22,7496.98,7447.35,7488.83,7275.84,7488.17,7396.38,7437.13,7540.59,7567.34,7522.83,7419.09,7523.84,7447.05,7465.61,7533.4,7406.44,7330.73,7249.58,7495.75,7421.88,7527.5,7249.58,7567.34
|
||||||
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
|
2024-04-23,7500.82,7467.03,7481.53,7301.37,7502.22,7423.83,7450.05,7529.01,7554.42,7525.62,7421.7,7519.67,7474.19,7493.49,7518.56,7409.24,7334.89,7301.52,7508.02,7407.19,7510.0,7301.37,7554.42
|
||||||
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
|
2024-04-24,7497.27,7486.78,7459.8,7321.17,7496.63,7431.4,7476.97,7509.26,7532.07,7519.69,7424.67,7500.56,7480.36,7501.79,7492.2,7399.97,7339.95,7322.99,7501.93,7410.32,7487.5,7321.17,7532.07
|
||||||
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
|
2024-04-25,7494.2,7498.19,7429.77,7313.03,7478.92,7441.51,7488.24,7489.95,7505.15,7494.85,7432.85,7474.95,7478.76,7484.28,7477.99,7392.8,7344.5,7300.05,7481.18,7420.67,7487.5,7300.05,7505.15
|
||||||
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
|
2024-04-26,7487.55,7506.31,7433.02,7316.84,7472.8,7448.11,7513.83,7482.12,7512.5,7489.62,7445.66,7481.97,7489.84,7486.69,7476.71,7395.86,7347.35,7301.56,7477.9,7450.74,7502.5,7301.56,7513.83
|
||||||
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
|
2024-04-28,7492.31,7520.16,7453.0,7303.44,7481.64,7470.31,7526.84,7501.31,7531.63,7497.66,7466.35,7499.28,7485.05,7472.34,7496.09,7403.69,7351.5,7297.57,7486.34,7491.89,7527.5,7297.57,7531.63
|
||||||
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
|
2024-04-29,7498.57,7544.38,7486.26,7274.8,7495.67,7531.22,7528.19,7533.16,7564.23,7521.28,7480.16,7535.52,7457.69,7502.54,7512.45,7415.22,7355.18,7300.0,7503.48,7512.67,7537.5,7274.8,7564.23
|
||||||
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
|
2024-04-30,7495.75,7567.61,7496.31,7291.07,7501.83,7521.1,7525.03,7545.76,7570.16,7542.07,7482.35,7551.96,7438.24,7535.52,7529.03,7422.59,7359.61,7294.48,7509.82,7520.88,7532.5,7291.07,7570.16
|
||||||
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
|
2024-05-06,7486.98,7579.82,7488.82,7321.15,7496.14,7475.81,7510.93,7539.36,7560.82,7539.02,7472.63,7538.92,7425.33,7523.91,7531.51,7425.09,7365.14,7341.47,7510.87,7504.35,7502.5,7321.15,7579.82
|
||||||
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
|
2024-05-07,7459.47,7534.61,7446.9,7327.92,7472.45,7416.02,7473.61,7500.68,7517.77,7513.82,7461.03,7495.62,7401.71,7499.71,7493.95,7423.25,7371.31,7381.04,7482.75,7427.8,7475.0,7327.92,7534.61
|
||||||
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
|
2024-05-08,7452.1,7498.89,7412.88,7315.18,7451.0,7393.7,7456.99,7466.12,7491.15,7492.59,7451.36,7466.67,7400.62,7459.54,7476.41,7422.86,7377.75,7356.28,7457.2,7413.32,7512.5,7315.18,7498.89
|
||||||
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
|
2024-05-09,7507.17,7475.33,7470.25,7304.88,7474.45,7402.05,7487.83,7516.03,7551.66,7514.2,7457.21,7515.92,7427.3,7461.2,7499.92,7434.42,7384.64,7346.23,7479.38,7489.75,7505.0,7304.88,7551.66
|
||||||
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
|
2024-05-10,7513.2,7484.36,7449.28,7318.6,7485.6,7421.4,7469.69,7498.91,7523.2,7512.77,7449.65,7501.96,7431.21,7468.93,7501.23,7437.45,7392.34,7332.98,7475.75,7494.29,7477.5,7318.6,7523.2
|
||||||
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
|
2024-05-11,7483.99,7467.86,7416.67,7329.71,7474.62,7444.24,7451.12,7474.21,7495.17,7500.56,7435.76,7469.77,7434.19,7468.85,7475.62,7438.88,7400.1,7335.73,7463.97,7463.24,7475.0,7329.71,7500.56
|
||||||
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
|
2024-05-13,7492.02,7464.54,7417.52,7339.22,7473.29,7459.2,7460.44,7485.61,7499.11,7486.36,7435.42,7462.95,7449.81,7487.18,7477.07,7448.36,7408.13,7388.08,7470.81,7462.39,7485.0,7339.22,7499.11
|
||||||
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
|
2024-05-14,7485.84,7469.97,7430.62,7384.12,7479.87,7489.41,7462.96,7479.66,7511.71,7483.63,7457.87,7468.02,7482.82,7489.58,7495.8,7450.75,7417.39,7436.82,7475.31,7470.97,7507.5,7384.12,7511.71
|
||||||
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
|
2024-05-15,7494.66,7522.62,7462.12,7474.2,7498.05,7496.8,7487.66,7508.05,7541.39,7503.29,7486.67,7493.37,7518.69,7500.46,7501.49,7471.96,7422.56,7433.88,7495.48,7474.04,7510.0,7422.56,7541.39
|
||||||
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
|
2024-05-16,7486.32,7540.05,7461.93,7457.79,7505.98,7500.59,7506.94,7517.17,7536.9,7516.66,7504.8,7500.54,7524.96,7515.96,7497.25,7475.93,7426.16,7438.5,7509.39,7487.81,7525.0,7426.16,7540.05
|
||||||
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
|
2024-05-17,7495.27,7546.01,7484.75,7385.87,7512.85,7512.16,7527.19,7534.39,7561.08,7537.95,7518.0,7529.81,7511.87,7526.4,7519.39,7487.89,7428.07,7443.66,7520.01,7495.98,7587.5,7385.87,7561.08
|
||||||
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
|
2024-05-20,7560.91,7634.81,7571.38,7369.94,7551.36,7540.14,7599.44,7615.53,7644.78,7640.54,7542.06,7609.56,7537.17,7538.88,7591.33,7608.87,7429.33,7438.36,7572.66,7580.6,7670.0,7369.94,7644.78
|
||||||
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
|
2024-05-21,7658.46,7694.73,7684.89,7399.57,7615.56,7572.75,7679.05,7723.06,7740.26,7767.59,7554.81,7719.04,7600.38,7599.58,7671.84,7656.84,7430.92,7491.58,7645.65,7667.59,7677.5,7399.57,7767.59
|
||||||
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
|
2024-05-22,7658.66,7698.43,7674.66,7424.53,7638.08,7568.24,7632.54,7712.51,7717.9,7771.7,7542.02,7724.91,7588.67,7629.96,7679.33,7656.76,7433.95,7554.48,7659.85,7672.34,7677.5,7424.53,7771.7
|
||||||
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
|
2024-05-23,7642.4,7701.71,7676.06,7429.57,7634.23,7552.03,7612.69,7712.66,7718.55,7767.44,7536.04,7722.24,7566.67,7651.84,7671.41,7581.26,7439.08,7547.6,7656.34,7653.24,7645.0,7429.57,7767.44
|
||||||
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
|
2024-05-24,7609.85,7671.89,7617.06,7431.3,7605.9,7513.65,7579.41,7654.46,7660.86,7738.27,7534.19,7674.64,7539.22,7650.42,7640.53,7529.25,7445.5,7534.8,7620.13,7598.74,7630.0,7431.3,7738.27
|
||||||
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
|
2024-05-27,7602.61,7593.42,7602.11,7423.0,7591.02,7509.58,7585.75,7619.81,7655.25,7679.82,7535.19,7648.6,7529.26,7610.52,7622.08,7536.4,7453.55,7516.73,7606.81,7607.49,7650.0,7423.0,7679.82
|
||||||
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
|
2024-05-28,7621.53,7583.29,7632.67,7445.46,7604.76,7536.8,7604.37,7666.65,7688.61,7713.19,7536.23,7676.49,7535.3,7602.96,7628.69,7561.36,7461.99,7546.64,7623.2,7637.36,7690.0,7445.46,7713.19
|
||||||
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
|
2024-05-29,7660.31,7578.23,7690.2,7504.58,7642.38,7556.52,7635.98,7722.5,7740.62,7758.82,7553.63,7726.35,7564.89,7633.76,7662.29,7584.71,7470.65,7606.23,7655.15,7643.97,7690.0,7470.65,7758.82
|
||||||
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
|
2024-05-30,7659.5,7556.64,7679.95,7530.3,7652.8,7541.2,7625.44,7716.96,7723.41,7779.81,7572.16,7718.33,7566.44,7647.5,7675.07,7605.57,7479.0,7634.36,7666.5,7566.29,7716.67,7479.0,7779.81
|
||||||
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
|
2024-05-31,7676.04,7586.69,7721.02,7540.06,7669.84,7558.63,7640.45,7755.05,7767.45,7825.41,7589.76,7745.55,7604.26,7679.07,7714.9,7641.48,7486.41,7657.35,7694.39,7561.95,7740.0,7486.41,7825.41
|
||||||
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
|
2024-06-03,7690.19,7600.21,7744.59,7565.74,7692.21,7576.14,7643.83,7774.9,7783.75,7838.53,7598.59,7768.8,7652.97,7688.61,7744.76,7658.11,7493.1,7667.21,7713.29,7635.55,7720.0,7493.1,7838.53
|
||||||
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
|
2024-06-04,7676.58,7606.3,7709.75,7467.87,7686.42,7586.88,7633.54,7732.22,7745.49,7822.4,7601.33,7739.83,7638.93,7670.32,7704.17,7640.85,7498.54,7586.58,7702.71,7650.31,7650.0,7467.87,7822.4
|
||||||
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
|
2024-06-05,7617.42,7581.98,7608.16,7428.58,7636.97,7592.04,7619.76,7642.54,7645.94,7710.69,7591.81,7655.94,7600.55,7656.55,7622.66,7582.8,7502.11,7543.27,7640.7,7558.49,7620.0,7428.58,7710.69
|
||||||
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
|
2024-06-06,7627.55,7588.77,7581.97,7433.4,7602.71,7600.4,7617.53,7613.13,7635.79,7570.58,7579.36,7631.12,7591.91,7629.57,7600.04,7577.28,7504.83,7526.28,7594.44,7584.87,7600.0,7433.4,7635.79
|
||||||
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
|
2024-06-07,7620.12,7566.53,7555.8,7388.42,7587.86,7584.3,7616.1,7584.17,7614.55,7535.23,7568.25,7605.75,7595.99,7578.05,7587.0,7574.39,7507.68,7516.18,7578.68,7597.59,7606.67,7388.42,7620.12
|
||||||
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
|
2024-06-11,7615.42,7559.85,7572.2,7409.47,7592.89,7608.43,7623.98,7598.93,7636.73,7560.02,7573.57,7615.37,7596.81,7575.98,7597.5,7578.83,7510.7,7524.21,7589.47,7608.48,7620.0,7409.47,7636.73
|
||||||
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
|
2024-06-12,7611.62,7558.03,7591.31,7445.59,7606.43,7639.72,7624.18,7636.33,7654.1,7637.44,7589.69,7633.55,7569.36,7605.97,7609.05,7591.22,7513.65,7462.63,7601.45,7616.41,7673.33,7445.59,7654.1
|
||||||
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
|
2024-06-13,7655.61,7626.9,7674.73,7463.02,7641.07,7638.81,7672.45,7713.7,7731.49,7805.59,7621.79,7705.76,7603.03,7659.96,7673.02,7621.81,7518.18,7520.33,7645.12,7648.14,7690.0,7463.02,7805.59
|
||||||
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
|
2024-06-14,7662.58,7664.25,7685.94,7544.92,7660.44,7647.96,7682.6,7725.29,7731.85,7828.61,7646.59,7720.59,7639.52,7679.22,7692.25,7660.54,7522.43,7577.3,7665.64,7660.87,7690.0,7522.43,7828.61
|
||||||
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
|
2024-06-17,7671.29,7645.47,7684.84,7542.79,7665.59,7665.5,7691.83,7715.76,7728.39,7830.33,7664.45,7720.84,7657.3,7667.72,7690.86,7643.12,7526.77,7592.37,7679.22,7629.43,7656.67,7526.77,7830.33
|
||||||
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
|
2024-06-18,7654.17,7660.78,7629.33,7515.05,7644.06,7636.13,7666.63,7667.54,7672.72,7798.64,7652.11,7680.42,7619.72,7641.58,7656.95,7592.23,7531.03,7556.47,7657.63,7638.64,7656.67,7515.05,7798.64
|
||||||
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
|
2024-06-19,7727.11,7643.68,7638.93,7486.67,7636.68,7591.02,7659.72,7664.19,7690.23,7756.19,7638.0,7681.55,7618.03,7634.64,7654.33,7591.34,7534.19,7558.35,7636.73,7637.2,7670.0,7486.67,7756.19
|
||||||
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
|
2024-06-20,7756.39,7671.19,7656.39,7551.18,7645.7,7595.62,7650.33,7687.96,7706.14,7773.77,7634.11,7701.54,7642.69,7644.28,7666.27,7593.11,7537.59,7590.28,7632.11,7650.3,7680.0,7537.59,7773.77
|
||||||
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
|
2024-06-21,7726.67,7679.87,7669.65,7712.56,7659.75,7610.74,7654.26,7698.64,7717.54,7814.91,7636.11,7712.27,7668.85,7668.87,7677.59,7596.45,7541.9,7629.76,7647.91,7660.17,7680.0,7541.9,7814.91
|
||||||
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
|
2024-06-24,7694.64,7690.54,7667.38,7627.26,7668.28,7625.21,7646.98,7705.2,7713.55,7819.28,7629.14,7707.03,7645.71,7680.38,7684.28,7592.7,7547.39,7624.03,7666.29,7648.92,7680.0,7547.39,7819.28
|
||||||
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
|
2024-06-25,7656.97,7679.24,7667.81,7571.17,7669.49,7652.55,7639.3,7702.86,7714.04,7804.54,7638.38,7698.53,7635.15,7695.28,7680.91,7592.76,7553.09,7628.98,7677.07,7634.02,7693.33,7553.09,7804.54
|
||||||
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
|
2024-06-26,7663.05,7701.06,7688.21,7562.37,7675.82,7671.93,7645.04,7717.94,7733.98,7810.16,7655.01,7714.51,7645.3,7714.43,7695.17,7603.42,7558.37,7637.97,7691.75,7632.18,7716.67,7558.37,7810.16
|
||||||
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
|
2024-06-27,7677.4,7725.59,7717.34,7581.08,7688.27,7684.8,7672.33,7746.99,7762.31,7828.62,7679.11,7743.03,7653.37,7703.94,7708.81,7629.43,7562.85,7637.44,7699.69,7625.66,7723.33,7562.85,7828.62
|
||||||
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
|
2024-06-28,7677.32,7752.75,7720.51,7657.93,7694.57,7678.53,7677.87,7750.27,7761.7,7842.71,7690.22,7751.73,7651.4,7685.27,7712.89,7630.63,7567.11,7647.67,7704.47,7616.63,7730.0,7567.11,7842.71
|
||||||
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
|
2024-07-01,7707.93,7784.59,7729.01,7713.53,7698.83,7678.79,7708.14,7757.56,7769.41,7858.08,7695.6,7759.5,7666.04,7688.93,7720.96,7649.1,7571.8,7667.3,7715.52,7664.76,7763.33,7571.8,7858.08
|
||||||
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
|
2024-07-02,7751.55,7828.23,7773.13,7724.23,7722.98,7680.78,7739.17,7797.09,7808.54,7880.75,7688.1,7793.55,7695.02,7722.0,7756.72,7715.2,7578.22,7720.69,7744.77,7741.74,7780.0,7578.22,7880.75
|
||||||
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
|
2024-07-03,7762.43,7804.35,7785.83,7760.37,7737.08,7688.67,7737.38,7805.72,7814.84,7884.52,7670.87,7805.74,7699.38,7736.65,7780.01,7781.53,7586.5,7744.31,7747.47,7776.13,7800.0,7586.5,7884.52
|
||||||
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
|
2024-07-04,7786.08,7819.25,7808.32,7806.04,7757.09,7698.67,7767.39,7826.63,7834.39,7886.31,7664.54,7824.24,7716.39,7755.69,7793.56,7744.54,7593.05,7766.06,7767.25,7795.52,7800.0,7593.05,7886.31
|
||||||
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
|
2024-07-05,7786.85,7843.45,7802.07,7826.74,7765.0,7704.08,7775.88,7824.34,7824.23,7867.37,7675.28,7822.93,7717.42,7771.03,7787.48,7722.59,7600.43,7758.61,7781.0,7773.13,7766.67,7600.43,7867.37
|
||||||
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
|
2024-07-08,7750.86,7799.34,7756.63,7791.87,7745.48,7728.45,7735.1,7771.43,7782.12,7830.41,7681.47,7783.1,7680.52,7766.86,7756.73,7673.49,7609.94,7700.99,7760.92,7733.4,7746.67,7609.94,7830.41
|
||||||
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
|
2024-07-09,7722.96,7782.81,7738.08,7732.26,7723.15,7740.89,7719.52,7758.3,7770.58,7808.82,7683.0,7764.36,7650.72,7738.94,7735.36,7674.87,7620.08,7663.38,7736.34,7708.61,7720.0,7620.08,7808.82
|
||||||
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
|
2024-07-10,7678.34,7696.34,7704.44,7661.36,7701.77,7712.37,7697.39,7727.91,7738.12,7775.17,7671.74,7736.38,7618.9,7709.76,7703.35,7668.79,7629.88,7638.96,7704.73,7692.53,7693.33,7618.9,7775.17
|
||||||
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
|
2024-07-11,7648.69,7678.83,7673.89,7641.02,7676.21,7708.96,7680.71,7693.04,7711.88,7753.58,7666.78,7706.51,7619.0,7682.29,7677.71,7681.14,7638.24,7626.26,7672.21,7680.78,7686.67,7619.0,7753.58
|
||||||
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
|
2024-07-12,7669.61,7653.02,7671.48,7638.26,7665.51,7658.81,7675.2,7701.66,7714.63,7765.84,7667.92,7701.91,7626.23,7678.42,7672.11,7680.77,7646.87,7629.74,7658.77,7681.25,7680.0,7626.23,7765.84
|
||||||
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
|
2024-07-15,7674.74,7636.31,7662.9,7620.0,7664.41,7649.86,7662.4,7692.31,7707.55,7758.14,7666.85,7694.71,7642.93,7675.73,7670.65,7683.32,7654.86,7640.19,7658.65,7685.69,7680.0,7620.0,7758.14
|
||||||
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
|
2024-07-16,7651.22,7656.75,7666.36,7551.27,7669.12,7662.39,7662.32,7694.89,7712.67,7768.03,7660.2,7693.56,7646.65,7667.06,7679.83,7685.51,7658.95,7640.29,7669.74,7726.9,7666.67,7551.27,7768.03
|
||||||
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
|
2024-07-17,7637.9,7658.34,7645.18,7595.97,7665.03,7655.32,7661.53,7680.92,7691.54,7757.11,7655.44,7673.35,7649.5,7651.11,7662.8,7670.56,7658.84,7649.71,7665.27,7701.17,7660.0,7595.97,7757.11
|
||||||
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
|
2024-07-18,7661.33,7667.48,7639.5,7661.17,7661.71,7662.09,7657.96,7670.44,7689.36,7759.37,7647.34,7671.03,7659.78,7652.43,7661.6,7657.73,7658.27,7662.58,7657.27,7689.07,7656.67,7639.5,7759.37
|
||||||
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
|
2024-07-19,7665.03,7653.72,7635.24,7671.45,7662.77,7660.37,7654.84,7669.45,7686.07,7754.96,7643.72,7669.79,7665.58,7643.64,7663.61,7649.73,7657.58,7676.53,7657.13,7685.6,7656.67,7635.24,7754.96
|
||||||
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
|
2024-07-22,7649.73,7652.24,7636.49,7640.14,7665.12,7666.42,7654.45,7668.51,7688.3,7764.49,7644.38,7672.99,7672.46,7658.74,7658.42,7642.28,7658.03,7679.9,7656.29,7667.09,7640.0,7636.49,7764.49
|
||||||
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
|
2024-07-23,7615.23,7649.73,7610.57,7745.08,7652.1,7662.34,7634.09,7645.28,7662.61,7733.56,7646.65,7650.71,7654.92,7659.24,7642.47,7624.17,7658.48,7666.79,7638.33,7656.65,7623.33,7610.57,7745.08
|
||||||
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
|
2024-07-24,7597.53,7648.63,7590.27,7709.71,7634.82,7637.72,7616.98,7624.78,7646.08,7661.27,7642.15,7625.71,7641.44,7656.11,7626.19,7609.78,7657.31,7655.85,7623.34,7656.74,7623.33,7590.27,7709.71
|
||||||
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
|
2024-07-25,7608.65,7628.18,7593.96,7721.59,7627.76,7630.19,7616.05,7628.94,7653.01,7653.68,7631.39,7626.97,7640.3,7666.61,7627.16,7611.94,7654.89,7646.36,7621.39,7660.64,7616.67,7593.96,7721.59
|
||||||
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
|
2024-07-26,7617.22,7628.52,7583.26,7709.2,7622.01,7624.53,7601.33,7616.61,7641.8,7622.3,7619.22,7615.34,7640.72,7653.16,7615.57,7609.45,7652.22,7641.88,7611.62,7667.77,7606.67,7583.26,7709.2
|
||||||
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
|
2024-07-29,7632.45,7611.93,7571.18,7708.48,7611.68,7620.21,7594.82,7607.42,7631.3,7611.93,7608.27,7602.7,7638.04,7621.31,7607.55,7595.9,7648.62,7652.41,7607.82,7630.84,7593.33,7571.18,7708.48
|
||||||
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
|
2024-07-30,7621.05,7604.68,7554.07,7703.14,7599.03,7601.6,7588.37,7595.38,7615.72,7591.75,7594.45,7583.41,7629.58,7602.19,7598.93,7574.65,7644.22,7648.83,7596.26,7617.22,7593.33,7554.07,7703.14
|
||||||
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
|
2024-07-31,7587.46,7606.04,7557.67,7670.79,7593.74,7595.79,7589.72,7594.85,7621.85,7591.03,7581.08,7582.76,7605.31,7587.62,7602.61,7569.93,7640.18,7615.0,7593.87,7608.1,7583.33,7557.67,7670.79
|
||||||
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
|
2024-08-01,7576.43,7592.65,7542.38,7665.73,7583.79,7575.55,7576.5,7581.67,7606.06,7578.42,7566.75,7567.78,7578.68,7581.86,7588.04,7561.38,7637.31,7580.62,7580.26,7601.32,7620.0,7542.38,7665.73
|
||||||
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
|
2024-08-02,7616.25,7594.54,7601.53,7705.82,7607.2,7603.66,7609.55,7638.54,7668.72,7714.11,7574.41,7622.81,7587.85,7601.98,7625.29,7605.34,7637.31,7588.98,7606.16,7607.22,7623.33,7574.41,7714.11
|
||||||
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
|
2024-08-05,7621.53,7594.39,7595.34,7724.07,7621.36,7617.55,7611.03,7638.07,7653.23,7735.43,7591.81,7625.9,7595.08,7607.4,7626.78,7606.12,7638.34,7605.8,7617.05,7605.43,7610.0,7591.81,7735.43
|
||||||
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
|
2024-08-06,7614.82,7597.06,7578.08,7718.44,7616.64,7609.5,7607.33,7612.45,7636.6,7706.2,7602.74,7605.37,7605.46,7622.93,7609.92,7603.31,7638.5,7617.91,7621.72,7609.77,7596.67,7578.08,7718.44
|
||||||
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
|
2024-08-07,7600.42,7592.15,7560.49,7712.35,7603.22,7600.74,7594.67,7605.4,7620.27,7632.15,7603.02,7587.78,7602.43,7620.43,7599.1,7588.53,7637.44,7606.83,7607.23,7602.89,7586.67,7560.49,7712.35
|
||||||
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
|
2024-08-08,7582.46,7576.33,7547.9,7699.7,7590.51,7583.07,7585.2,7581.87,7610.47,7580.54,7597.01,7572.43,7580.94,7599.9,7587.52,7592.21,7634.14,7609.82,7591.49,7595.2,7583.33,7547.9,7699.7
|
||||||
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
|
2024-08-09,7572.3,7574.92,7544.76,7687.6,7581.03,7579.99,7585.11,7579.56,7609.39,7579.0,7593.58,7571.36,7577.72,7584.52,7578.71,7580.27,7630.39,7602.62,7580.79,7613.95,7583.33,7544.76,7687.6
|
||||||
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
|
2024-08-12,7565.19,7561.48,7545.41,7675.48,7579.53,7568.37,7583.51,7586.38,7610.8,7581.76,7591.17,7573.16,7571.12,7579.25,7574.68,7569.9,7626.75,7611.49,7579.89,7606.85,7586.67,7545.41,7675.48
|
||||||
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
|
2024-08-13,7563.62,7570.16,7550.8,7670.79,7579.66,7581.39,7587.7,7591.05,7616.26,7595.69,7589.5,7581.39,7578.19,7586.37,7580.72,7572.45,7624.13,7622.5,7584.15,7605.62,7593.33,7550.8,7670.79
|
||||||
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
|
2024-08-14,7576.39,7574.62,7560.34,7667.32,7581.9,7581.96,7593.26,7601.19,7625.12,7601.06,7585.99,7582.96,7591.15,7583.67,7594.52,7589.33,7621.85,7623.16,7588.09,7596.56,7590.0,7560.34,7667.32
|
||||||
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
|
2024-08-15,7577.48,7576.58,7554.1,7660.54,7580.27,7581.07,7588.06,7594.36,7617.3,7594.63,7577.62,7574.04,7585.9,7573.04,7589.19,7595.72,7619.14,7616.89,7584.21,7588.85,7583.33,7554.1,7660.54
|
||||||
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
|
2024-08-16,7575.3,7564.88,7545.8,7663.72,7575.85,7576.91,7580.0,7585.32,7609.72,7583.25,7570.84,7568.21,7574.59,7570.58,7584.68,7589.57,7615.98,7611.6,7577.83,7587.43,7583.33,7545.8,7663.72
|
||||||
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
|
2024-08-19,7583.78,7568.77,7547.04,7670.54,7576.66,7590.53,7583.24,7587.57,7611.94,7580.78,7566.84,7568.77,7576.56,7563.43,7580.76,7592.1,7612.88,7618.86,7577.68,7592.79,7570.0,7547.04,7670.54
|
||||||
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
|
2024-08-20,7572.15,7545.57,7526.81,7634.95,7568.6,7580.24,7569.61,7567.35,7591.95,7568.04,7561.51,7550.82,7574.94,7559.89,7564.28,7585.51,7609.26,7617.66,7564.85,7593.95,7570.0,7526.81,7634.95
|
||||||
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
|
2024-08-21,7573.42,7546.46,7530.82,7647.96,7567.89,7575.96,7578.4,7571.05,7598.86,7567.66,7559.77,7551.3,7579.86,7556.56,7568.24,7577.28,7605.51,7615.94,7567.96,7595.48,7570.0,7530.82,7647.96
|
||||||
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
|
2024-08-22,7573.07,7563.47,7529.61,7646.94,7570.14,7576.54,7578.19,7572.59,7597.05,7564.06,7557.64,7549.96,7582.79,7558.09,7571.36,7573.0,7601.59,7617.35,7570.82,7596.41,7590.0,7529.61,7646.94
|
||||||
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
|
2024-08-23,7591.57,7572.93,7560.14,7645.47,7585.53,7577.36,7595.08,7597.17,7628.08,7602.95,7562.97,7578.88,7588.25,7580.61,7588.53,7588.33,7598.58,7623.95,7586.39,7599.99,7583.33,7560.14,7645.47
|
||||||
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
|
2024-08-26,7585.94,7562.26,7544.76,7635.78,7584.95,7583.38,7584.8,7588.16,7607.8,7587.92,7569.21,7568.6,7581.87,7588.27,7584.7,7584.03,7595.39,7613.6,7582.57,7594.95,7583.33,7544.76,7635.78
|
||||||
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
|
2024-08-27,7583.67,7554.79,7548.9,7645.81,7583.01,7588.14,7583.17,7588.62,7614.19,7590.1,7572.24,7571.03,7577.78,7596.16,7581.18,7582.94,7592.44,7608.61,7585.5,7587.22,7610.0,7548.9,7645.81
|
||||||
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
|
2024-08-28,7608.75,7575.75,7587.12,7668.45,7599.94,7608.28,7606.73,7628.21,7652.47,7664.54,7591.59,7610.38,7596.44,7602.33,7610.44,7591.02,7590.88,7622.72,7604.31,7595.06,7610.0,7575.75,7668.45
|
||||||
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
|
2024-08-29,7610.24,7582.3,7579.49,7672.68,7606.13,7611.64,7601.7,7614.17,7638.98,7657.09,7608.48,7604.72,7599.74,7592.46,7610.07,7593.12,7590.19,7628.86,7610.39,7598.34,7613.33,7579.49,7672.68
|
||||||
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
|
2024-08-30,7614.03,7595.25,7586.96,7657.08,7609.34,7627.82,7609.66,7622.84,7647.73,7683.32,7625.26,7611.68,7604.11,7604.98,7607.4,7605.44,7590.4,7627.31,7616.44,7604.42,7606.67,7586.96,7683.32
|
||||||
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
|
2024-09-02,7605.77,7602.34,7574.01,7646.35,7606.13,7623.34,7601.08,7617.14,7632.79,7647.45,7631.62,7601.88,7607.32,7602.92,7610.32,7598.03,7590.24,7622.12,7604.07,7593.73,7583.33,7574.01,7647.45
|
||||||
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
|
2024-09-03,7585.53,7590.18,7541.28,7624.92,7585.56,7597.08,7575.22,7577.4,7602.02,7584.32,7620.38,7566.19,7593.28,7584.09,7584.79,7569.87,7589.39,7616.06,7579.08,7589.84,7562.5,7541.28,7624.92
|
||||||
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
|
2024-09-04,7568.37,7569.2,7515.98,7612.19,7565.79,7581.26,7562.25,7558.08,7581.85,7545.27,7604.7,7535.46,7576.39,7572.94,7565.45,7557.33,7587.47,7606.64,7560.98,7584.89,7535.0,7515.98,7612.19
|
||||||
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
|
2024-09-05,7543.76,7553.68,7480.27,7604.09,7542.46,7560.43,7542.41,7526.06,7549.62,7514.26,7580.73,7496.51,7554.42,7552.65,7529.62,7533.45,7583.88,7591.8,7536.19,7604.62,7475.0,7480.27,7604.62
|
||||||
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
|
2024-09-06,7486.44,7535.7,7399.34,7585.71,7490.15,7530.32,7484.73,7455.8,7477.09,7467.9,7540.53,7420.37,7514.24,7518.1,7469.82,7508.77,7576.25,7556.39,7483.85,7606.65,7442.5,7399.34,7606.65
|
||||||
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
|
2024-09-09,7470.01,7518.78,7364.62,7568.0,7446.27,7498.53,7460.67,7431.29,7457.63,7439.77,7496.37,7380.39,7490.04,7478.05,7433.33,7489.86,7566.61,7575.42,7447.27,7583.34,7422.5,7364.62,7583.34
|
||||||
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
|
2024-09-10,7451.33,7504.45,7339.71,7544.56,7420.48,7477.1,7438.77,7409.71,7440.34,7417.87,7462.65,7355.09,7458.54,7445.15,7421.15,7475.16,7556.29,7554.15,7425.45,7562.5,7412.5,7339.71,7562.5
|
||||||
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
|
2024-09-11,7430.97,7486.7,7330.97,7520.38,7404.28,7462.26,7420.89,7398.85,7436.14,7408.15,7431.1,7340.45,7432.55,7418.98,7415.28,7464.61,7546.08,7537.96,7417.44,7535.0,7387.5,7330.97,7546.08
|
||||||
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
|
2024-09-12,7398.0,7476.79,7303.67,7486.69,7382.31,7444.67,7391.09,7382.94,7411.65,7396.88,7397.04,7313.15,7402.38,7400.79,7386.94,7448.76,7534.85,7515.68,7395.3,7475.01,7380.0,7303.67,7534.85
|
||||||
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
|
2024-09-13,7388.71,7460.05,7300.95,7442.77,7369.45,7424.93,7380.86,7379.25,7411.88,7392.85,7373.98,7309.57,7382.1,7401.54,7385.04,7438.55,7523.98,7489.61,7384.62,7442.51,7395.0,7300.95,7523.98
|
||||||
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
|
2024-09-14,7403.95,7460.29,7318.92,7418.46,7380.9,7417.35,7390.87,7396.28,7429.07,7406.03,7370.9,7327.5,7375.1,7403.94,7403.98,7435.49,7514.86,7488.97,7393.19,7422.53,7375.0,7318.92,7514.86
|
||||||
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
|
2024-09-18,7378.25,7448.75,7296.64,7395.37,7374.33,7397.35,7365.31,7369.65,7403.89,7405.31,7366.79,7307.74,7352.11,7382.56,7384.77,7418.46,7505.07,7474.06,7382.2,7412.61,7342.5,7296.64,7505.07
|
||||||
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
|
2024-09-19,7345.8,7426.26,7273.03,7361.27,7351.39,7367.71,7348.02,7343.67,7377.78,7405.74,7364.26,7286.64,7340.79,7371.55,7346.23,7404.98,7493.85,7464.35,7357.08,7392.25,7325.0,7273.03,7493.85
|
||||||
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
|
2024-09-20,7331.34,7408.45,7261.47,7322.75,7333.1,7352.78,7340.24,7332.29,7366.28,7407.88,7365.96,7275.0,7339.29,7357.37,7336.36,7381.77,7482.18,7441.5,7337.64,7389.01,7340.0,7261.47,7482.18
|
||||||
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
|
2024-09-23,7350.17,7408.47,7273.6,7313.24,7338.57,7354.65,7355.94,7342.36,7383.53,7413.67,7372.76,7285.44,7350.24,7359.13,7346.39,7390.63,7472.17,7422.84,7336.25,7363.92,7310.0,7273.6,7472.17
|
||||||
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
|
2024-09-24,7334.33,7386.49,7250.34,7286.01,7321.56,7341.2,7323.07,7313.94,7351.58,7405.47,7369.24,7258.87,7332.21,7339.95,7319.59,7372.23,7460.94,7377.13,7309.53,7330.44,7280.0,7250.34,7460.94
|
||||||
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
|
2024-09-25,7331.68,7361.19,7233.76,7242.51,7295.02,7325.46,7313.92,7295.67,7333.57,7389.7,7358.17,7239.93,7329.29,7318.18,7284.22,7352.1,7448.6,7356.73,7292.13,7300.88,7315.0,7233.76,7448.6
|
||||||
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
|
2024-09-26,7383.23,7352.07,7258.63,7293.74,7311.57,7337.57,7346.59,7334.2,7368.18,7395.19,7360.15,7262.58,7348.37,7337.87,7321.63,7365.79,7438.92,7466.29,7315.96,7309.91,7310.0,7258.63,7466.29
|
||||||
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
|
2024-09-27,7395.39,7339.83,7252.57,7276.39,7311.78,7332.55,7328.61,7316.26,7355.87,7383.46,7355.37,7250.85,7333.29,7314.8,7318.06,7363.19,7429.49,7486.45,7307.61,7323.36,7310.0,7250.85,7486.45
|
||||||
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
|
2024-09-29,7404.69,7324.63,7255.95,7317.26,7310.44,7339.92,7335.89,7322.03,7360.7,7391.28,7348.52,7255.08,7324.23,7308.09,7314.78,7356.94,7420.04,7486.51,7314.78,7323.36,7365.0,7255.08,7486.51
|
||||||
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
|
2024-09-30,7426.12,7346.58,7300.09,7466.31,7351.28,7360.36,7371.79,7386.81,7413.94,7423.46,7354.2,7305.8,7350.79,7337.02,7372.67,7375.01,7413.55,7561.84,7362.78,7362.27,7420.0,7300.09,7561.84
|
||||||
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
|
2024-10-08,7406.37,7405.57,7363.97,7515.05,7401.51,7391.97,7409.69,7428.8,7463.3,7450.62,7382.75,7368.46,7376.8,7366.11,7426.31,7404.72,7409.51,7624.95,7412.17,7419.09,7602.5,7363.97,7624.95
|
||||||
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
|
2024-10-09,7577.33,7575.14,7625.6,7581.14,7544.47,7607.6,7631.74,7679.46,7699.37,7713.1,7580.66,7635.88,7586.54,7484.3,7607.45,7604.1,7409.56,7772.99,7573.71,7602.24,7602.5,7409.56,7772.99
|
||||||
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
|
2024-10-10,7576.0,7600.38,7581.64,7710.07,7608.25,7677.35,7632.34,7639.99,7633.43,7766.71,7661.64,7634.72,7640.36,7542.83,7609.24,7617.74,7409.71,7778.78,7604.18,7602.37,7585.0,7409.71,7778.78
|
||||||
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
|
2024-10-11,7631.65,7629.16,7575.07,7828.43,7609.2,7714.27,7629.44,7599.98,7624.22,7788.7,7692.1,7618.88,7651.66,7572.18,7587.86,7623.61,7409.73,7772.77,7600.31,7584.85,7575.0,7409.73,7828.43
|
||||||
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
|
2024-10-12,7653.07,7603.69,7548.5,7764.68,7585.18,7706.53,7607.08,7603.88,7602.92,7740.27,7701.24,7589.1,7663.18,7571.87,7574.65,7629.57,7409.63,7757.64,7568.07,7574.84,7576.67,7409.63,7764.68
|
||||||
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
|
2024-10-14,7691.78,7615.71,7548.03,7716.77,7552.13,7690.78,7583.72,7553.81,7607.37,7730.31,7697.61,7583.52,7677.65,7557.6,7562.15,7651.37,7409.52,7674.32,7546.73,7576.1,7565.0,7409.52,7730.31
|
||||||
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
|
2024-10-15,7639.02,7607.88,7524.41,7638.5,7538.07,7647.37,7550.89,7554.31,7587.3,7702.48,7679.03,7562.07,7640.33,7526.24,7536.04,7653.55,7409.2,7629.03,7536.11,7564.45,7527.5,7409.2,7702.48
|
||||||
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
|
2024-10-16,7560.16,7595.21,7472.19,7540.82,7510.13,7557.71,7500.61,7525.6,7538.71,7580.69,7621.43,7490.21,7571.23,7510.21,7483.04,7574.04,7408.31,7581.95,7510.9,7522.37,7477.5,7408.31,7621.43
|
||||||
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
|
2024-10-17,7497.05,7575.46,7406.36,7471.87,7462.23,7506.3,7433.55,7466.97,7484.21,7495.48,7552.38,7417.59,7502.19,7486.54,7451.01,7481.14,7406.41,7521.34,7464.61,7505.66,7467.5,7406.36,7575.46
|
||||||
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
|
2024-10-18,7485.83,7583.46,7400.38,7425.55,7439.71,7492.0,7417.06,7460.72,7487.02,7473.88,7504.52,7368.89,7465.12,7435.8,7465.36,7460.77,7404.29,7507.99,7448.06,7457.38,7370.0,7368.89,7583.46
|
||||||
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
|
2024-10-21,7344.19,7446.11,7278.98,7365.72,7369.4,7422.88,7311.05,7344.49,7375.82,7434.49,7406.53,7267.31,7378.31,7359.09,7361.07,7360.86,7399.08,7480.43,7359.83,7457.08,7362.5,7267.31,7480.43
|
||||||
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
|
2024-10-22,7325.96,7412.26,7285.18,7348.48,7343.75,7341.76,7318.72,7356.17,7398.84,7424.42,7348.22,7270.19,7359.63,7331.88,7344.37,7359.19,7393.72,7486.7,7339.82,7498.96,7346.67,7270.19,7498.96
|
||||||
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
|
2024-10-23,7300.58,7351.56,7269.19,7343.0,7330.7,7285.19,7316.86,7347.0,7376.53,7407.48,7302.44,7257.8,7344.1,7307.03,7315.78,7345.47,7387.87,7479.8,7328.57,7479.02,7357.5,7257.8,7479.8
|
||||||
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
|
2024-10-24,7327.36,7329.6,7282.53,7348.72,7335.02,7320.17,7344.86,7348.92,7396.87,7389.91,7289.68,7275.05,7332.82,7332.66,7343.01,7344.94,7382.52,7455.38,7340.73,7461.51,7352.5,7275.05,7461.51
|
||||||
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
|
2024-10-25,7343.94,7302.35,7278.58,7350.82,7347.83,7335.72,7367.26,7362.14,7388.07,7392.13,7272.65,7271.2,7327.52,7320.14,7359.31,7334.4,7377.2,7445.2,7356.27,7405.27,7372.5,7271.2,7445.2
|
||||||
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
|
2024-10-28,7348.35,7350.34,7299.95,7327.24,7373.17,7332.05,7394.25,7378.33,7413.72,7409.43,7278.52,7304.22,7355.17,7345.4,7380.76,7341.12,7372.85,7468.81,7385.35,7352.0,7373.33,7278.52,7468.81
|
||||||
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
|
2024-10-29,7362.26,7353.65,7299.38,7318.7,7382.63,7372.1,7388.31,7376.75,7408.46,7425.13,7306.8,7317.3,7360.51,7394.13,7388.48,7339.41,7368.71,7459.03,7374.2,7368.01,7380.0,7299.38,7459.03
|
||||||
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
|
2024-10-30,7368.73,7383.5,7309.24,7338.38,7385.75,7428.36,7376.78,7383.46,7418.25,7445.98,7354.42,7342.89,7363.36,7410.4,7387.01,7346.05,7365.06,7441.74,7359.37,7437.75,7330.0,7309.24,7445.98
|
||||||
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
|
2024-10-31,7311.11,7336.54,7264.33,7340.7,7354.56,7416.21,7295.6,7331.68,7363.08,7446.37,7349.75,7310.97,7345.95,7400.75,7339.24,7304.52,7359.86,7449.62,7331.52,7393.39,7340.0,7264.33,7449.62
|
||||||
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
|
2024-11-01,7325.45,7340.42,7277.19,7376.04,7350.09,7391.7,7325.08,7346.34,7386.1,7448.67,7361.73,7319.58,7370.1,7385.94,7339.78,7319.82,7355.4,7460.73,7337.9,7388.84,7363.33,7277.19,7460.73
|
||||||
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
|
2024-11-04,7380.95,7375.41,7292.43,7378.57,7362.64,7391.41,7347.38,7368.79,7402.84,7453.22,7370.37,7335.67,7402.01,7359.02,7359.95,7345.06,7352.02,7437.42,7359.38,7382.9,7360.0,7292.43,7453.22
|
||||||
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
|
2024-11-05,7384.76,7388.51,7289.12,7344.35,7359.37,7429.52,7351.29,7355.62,7396.87,7454.93,7371.28,7325.31,7409.42,7374.6,7372.07,7351.83,7348.71,7441.46,7351.21,7367.23,7357.5,7289.12,7454.93
|
||||||
2024-11-06,7372.14,7355.21,7288.76,7314.96,7358.52,7411.56,7351.78,7368.22,7396.45,7452.88,7354.85,7322.02,7400.71,7361.08,7378.69,7358.67,7361.11,7474.57,7344.45,7366.55,7383.33,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.01,7288.76,7474.57,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,7378.69,NLinear
|
2024-11-06,7372.14,7358.0,7288.76,7318.64,7358.52,7411.56,7351.78,7368.22,7396.45,7452.88,7354.75,7322.02,7400.71,7361.08,7378.69,7358.67,7345.52,7434.25,7344.45,7366.55,7383.33,7288.76,7452.88
|
||||||
2024-11-07,7394.49,7360.17,7314.98,7313.69,7372.23,7396.45,7372.83,7393.67,7424.33,7451.24,7358.23,7334.24,7417.74,7365.87,7401.06,7365.66,7359.13,7468.38,7365.83,7385.99,7366.67,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,7313.69,7468.38,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.01,0.01,0.0,0.01,7365.87,DLinear
|
2024-11-07,7394.49,7358.39,7314.98,7329.65,7372.23,7396.45,7372.83,7393.67,7424.33,7451.24,7358.02,7334.24,7417.74,7365.87,7401.06,7365.66,7343.64,7444.3,7365.83,7385.99,7366.67,7314.98,7451.24
|
||||||
2024-11-08,7367.07,7340.66,7293.82,7316.41,7367.46,7382.51,7359.92,7362.61,7398.7,7438.23,7354.98,7304.15,7401.83,7383.2,7376.13,7365.23,7357.25,7458.3,7358.59,7369.13,7383.33,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,7293.82,7458.3,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,7383.2,DLinear
|
2024-11-08,7367.07,7334.52,7293.82,7338.27,7367.46,7382.51,7359.92,7362.61,7398.7,7438.23,7354.75,7304.15,7401.83,7383.2,7376.13,7365.23,7341.77,7428.28,7358.59,7369.13,7383.33,7293.82,7438.23
|
||||||
2024-11-11,7385.33,7367.98,7315.88,7312.9,7378.33,7404.64,7387.22,7388.8,7424.18,7437.92,7365.14,7305.25,7419.91,7362.62,7385.12,7370.33,7356.28,7474.63,7378.79,7380.81,7340.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,7305.25,7474.63,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.01,0.0,0.0,0.02,7356.28,StemGNN
|
2024-11-11,7385.33,7363.97,7315.88,7338.59,7378.33,7404.64,7387.22,7388.8,7424.18,7437.92,7364.92,7305.25,7419.91,7362.62,7385.12,7370.33,7340.8,7429.16,7378.79,7380.81,7340.0,7305.25,7437.92
|
||||||
2024-11-12,7348.61,7291.9,7270.91,7276.3,7360.35,7418.77,7340.51,7341.66,7370.75,7421.61,7353.11,7263.97,7401.87,7368.74,7336.24,7330.87,7354.22,7450.79,7358.86,7365.14,7306.67,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.0,0.01,0.0,0.01,0.01,0.0,0.01,7263.97,7450.79,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.02,0.01,0.02,0.0,0.01,0.02,7291.9,Informer
|
2024-11-12,7348.61,7287.29,7270.91,7330.81,7360.35,7418.77,7340.51,7341.66,7370.75,7421.61,7353.07,7263.97,7401.87,7368.74,7336.24,7330.87,7338.66,7413.92,7358.86,7365.14,7306.67,7263.97,7421.61
|
||||||
2024-11-13,7332.29,7268.64,7251.3,7254.86,7321.8,7370.7,7316.54,7309.23,7352.06,7407.6,7345.42,7241.63,7403.21,7337.77,7293.72,7283.12,7351.63,7422.7,7318.66,7366.33,7323.33,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.01,7241.63,7422.7,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.01,0.0,0.01,0.01,0.0,0.01,7321.8,TSMixer
|
2024-11-13,7332.29,7265.2,7251.3,7311.66,7321.8,7370.7,7316.54,7309.23,7352.06,7407.6,7345.56,7241.63,7403.21,7337.77,7293.72,7283.12,7335.86,7393.37,7318.66,7366.33,7323.33,7241.63,7407.6
|
||||||
2024-11-14,7344.44,7272.3,7257.76,7244.21,7333.12,7360.61,7371.92,7303.81,7360.68,7413.77,7340.82,7254.99,7417.81,7357.29,7319.99,7274.54,7348.14,7390.92,7325.41,7400.55,7353.33,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.01,7244.21,7417.81,0.0,0.01,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.01,7357.29,DLinear
|
2024-11-14,7344.44,7268.3,7257.76,7310.26,7333.12,7360.61,7371.92,7303.81,7360.68,7413.77,7341.0,7254.99,7417.81,7357.29,7319.99,7274.54,7330.14,7394.09,7325.41,7400.55,7353.33,7254.99,7417.81
|
||||||
2024-11-15,7319.74,7273.8,7268.91,7230.74,7333.59,7348.71,7348.61,7284.0,7349.73,7441.16,7343.64,7265.12,7418.84,7321.15,7309.22,7271.93,7337.24,7383.45,7336.21,7391.44,7393.33,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.01,0.01,0.01,0.01,0.01,0.0,0.0,0.02,0.01,0.0,0.01,7230.74,7441.16,0.01,0.01,0.01,0.01,0.01,0.01,0.02,0.01,0.01,0.02,0.02,0.0,0.0,0.02,0.01,0.01,0.01,0.02,0.01,0.0,7391.44,DeepNPTS
|
2024-11-15,7319.74,7272.11,7268.91,7297.83,7333.59,7348.71,7348.61,7284.0,7349.73,7441.16,7343.65,7265.12,7418.84,7321.15,7309.22,7271.93,7330.99,7363.37,7336.21,7391.44,7393.33,7265.12,7441.16
|
||||||
2024-11-18,7317.36,7293.89,7282.1,7208.8,7330.95,7366.03,7319.87,7297.49,7368.11,7442.79,7338.93,7287.87,7411.82,7370.53,7321.93,7272.12,7360.05,7365.12,7326.46,7397.05,7363.33,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.02,0.01,0.01,0.01,7208.8,7442.79,0.01,0.01,0.01,0.0,0.01,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.01,0.0,0.0,0.01,0.02,0.0,0.0,7365.12,MLPMultivariate
|
2024-11-18,7317.36,7288.62,7282.1,7251.7,7330.95,7366.03,7319.87,7297.49,7368.11,7442.79,7339.01,7287.87,7411.82,7370.53,7321.93,7272.12,7334.82,7336.82,7326.46,7397.05,7363.33,7251.7,7442.79
|
||||||
2024-11-19,7307.74,7295.89,7310.31,7191.16,7332.0,7325.75,7319.12,7335.5,7371.58,7436.03,7333.44,7305.78,7369.13,7344.6,7332.86,7272.76,7349.71,7334.35,7334.74,7399.99,7370.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.02,0.02,0.01,0.0,7191.16,7436.03,0.01,0.0,0.0,0.01,0.01,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.0,0.01,0.01,0.0,0.01,0.02,0.0,0.0,7369.13,MLP
|
2024-11-19,7307.74,7292.03,7310.31,7204.05,7332.0,7325.75,7319.12,7335.5,7371.58,7436.03,7333.48,7305.78,7369.13,7344.6,7332.86,7272.76,7323.65,7325.47,7334.74,7399.99,7370.0,7204.05,7436.03
|
||||||
2024-11-20,7337.59,7316.53,7313.05,7342.47,7344.39,7306.89,7350.64,7378.3,7405.32,7418.54,7321.07,7320.57,7338.04,7387.81,7361.4,7362.29,7351.25,7331.53,7354.71,7376.74,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
|
2024-11-20,7337.59,7301.89,7313.05,7339.87,7344.39,7306.89,7350.64,7378.3,7405.32,7418.54,7321.05,7320.57,7338.04,7387.81,7361.4,7362.29,7335.48,7358.18,7354.71,7376.74,,7301.89,7418.54
|
||||||
2024-11-21,7317.17,7307.52,7316.97,7328.65,7332.21,7313.82,7338.19,7370.33,7411.06,7428.94,7317.81,7327.25,7336.69,7338.95,7351.55,7367.47,7347.77,7312.24,7317.22,7363.68,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
|
2024-11-21,7317.17,7301.47,7316.97,7335.4,7332.21,7313.82,7338.19,7370.33,7411.06,7428.94,7317.82,7327.25,7336.69,7338.95,7351.55,7367.47,7329.76,7347.35,7317.22,7363.68,,7301.47,7428.94
|
||||||
2024-11-22,7297.24,7303.13,7326.41,7326.86,7312.23,7317.62,7323.69,7346.49,7403.67,7445.02,7312.61,7330.36,7338.74,7334.72,7364.72,7364.16,7336.86,7308.51,7293.33,7345.68,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
|
2024-11-22,7297.24,7296.55,7326.41,7308.93,7312.23,7317.62,7323.69,7346.49,7403.67,7445.02,7312.55,7330.36,7338.74,7334.72,7364.72,7364.16,7330.61,7326.98,7293.33,7345.68,,7293.33,7445.02
|
||||||
2024-11-25,7303.15,7296.9,7344.72,7335.47,7295.32,7333.21,7318.15,7358.58,7420.51,7441.58,7302.02,7356.28,7322.75,7303.17,7395.47,7359.94,7359.68,7302.15,7295.56,7386.05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
|
2024-11-25,7303.15,7286.08,7344.72,7295.19,7295.32,7333.21,7318.15,7358.58,7420.51,7441.58,7301.84,7356.28,7322.75,7303.17,7395.47,7359.94,7334.44,7311.92,7295.56,7386.05,,7286.08,7441.58
|
||||||
2024-11-26,7291.95,7311.98,7373.82,7342.08,7280.39,7364.26,7291.85,7386.51,7420.56,7449.44,7294.83,7374.35,7319.28,7310.3,7329.59,7357.71,7349.34,7286.71,7291.14,7341.73,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
|
2024-11-26,7291.95,7299.42,7373.82,7290.42,7280.39,7364.26,7291.85,7386.51,7420.56,7449.44,7294.74,7374.35,7319.28,7310.3,7329.59,7357.71,7323.26,7293.3,7291.14,7341.73,,7280.39,7449.44
|
||||||
|
|||||||
|
@ -9,13 +9,13 @@ DilatedRNN,1841.868,42.917,31.132
|
|||||||
DLinear,1865.095,43.187,30.754
|
DLinear,1865.095,43.187,30.754
|
||||||
GRU,1948.117,44.137,37.899
|
GRU,1948.117,44.137,37.899
|
||||||
LSTM,3160.178,56.215,46.181
|
LSTM,3160.178,56.215,46.181
|
||||||
Informer,3291.08,57.368,42.942
|
Informer,3208.359,56.642,42.457
|
||||||
DeepNPTS,3342.861,57.817,42.448
|
DeepNPTS,3342.861,57.817,42.448
|
||||||
MLP,3377.778,58.119,44.678
|
MLP,3377.778,58.119,44.678
|
||||||
TFT,3480.96,59.0,45.242
|
TFT,3480.96,59.0,45.242
|
||||||
TSMixerx,4424.076,66.514,50.562
|
TSMixerx,4424.076,66.514,50.562
|
||||||
BiTCN,4426.452,66.532,51.306
|
BiTCN,4446.318,66.681,51.416
|
||||||
TCN,6855.79,82.8,68.446
|
TCN,6855.79,82.8,68.446
|
||||||
iTransformer,11338.897,106.484,86.463
|
MLPMultivariate,10183.86,100.915,79.735
|
||||||
StemGNN,12423.222,111.46,91.022
|
iTransformer,13646.124,116.817,93.078
|
||||||
MLPMultivariate,12947.138,113.785,84.447
|
StemGNN,13746.6,117.246,94.793
|
||||||
|
|||||||
|
@ -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
|
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
|
1,2024-11-20,7337.594,7301.8896,7313.051,7339.8745,7344.392,7306.8916,7350.6377,7378.303,7405.318,7418.5396,7321.0522,7320.571,7338.035,7387.8057,7361.405,7362.294,,7335.4824,7358.1826,7354.707,7376.7446
|
||||||
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
|
1,2024-11-21,7317.166,7301.467,7316.9746,7335.3994,7332.2124,7313.822,7338.1885,7370.3306,7411.06,7428.9434,7317.8223,7327.2505,7336.689,7338.9487,7351.548,7367.47,,7329.756,7347.353,7317.218,7363.681
|
||||||
1,2024-11-22,7297.237,7303.1304,7326.406,7326.8555,7312.2275,7317.625,7323.693,7346.49,7403.6733,7445.0225,7312.6143,7330.3555,7338.735,7334.716,7364.716,7364.164,,7336.8613,7308.5083,7293.3335,7345.6753
|
1,2024-11-22,7297.237,7296.5513,7326.406,7308.9272,7312.2275,7317.625,7323.693,7346.49,7403.6733,7445.0225,7312.5454,7330.3555,7338.735,7334.716,7364.716,7364.164,,7330.611,7326.976,7293.3335,7345.6753
|
||||||
1,2024-11-25,7303.1514,7296.904,7344.721,7335.4663,7295.3184,7333.2104,7318.1465,7358.585,7420.508,7441.583,7302.0195,7356.277,7322.7534,7303.1694,7395.4727,7359.9365,,7359.6836,7302.15,7295.5586,7386.0483
|
1,2024-11-25,7303.1514,7286.081,7344.721,7295.186,7295.3184,7333.2104,7318.1465,7358.585,7420.508,7441.583,7301.844,7356.277,7322.7534,7303.1694,7395.4727,7359.9365,,7334.437,7311.919,7295.5586,7386.0483
|
||||||
1,2024-11-26,7291.9507,7311.975,7373.8174,7342.075,7280.3906,7364.2593,7291.8506,7386.514,7420.5625,7449.439,7294.8267,7374.346,7319.2783,7310.3,7329.5864,7357.711,,7349.3394,7286.713,7291.1426,7341.7344
|
1,2024-11-26,7291.9507,7299.419,7373.8174,7290.4204,7280.3906,7364.2593,7291.8506,7386.514,7420.5625,7449.439,7294.745,7374.346,7319.2783,7310.3,7329.5864,7357.711,,7323.2603,7293.296,7291.1426,7341.7344
|
||||||
|
|||||||
|
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@ -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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@ -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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@ -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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2024-11-01,-346.669,,,,-1055.0,7310.0,8600.0,9598.0,10500.0,51.0,-229.6843,33.7,44.07,22.22,4.81,0.6593,5750.0,553.84,425.0,1134.78,8.13,6.6,7.7,46.1,46.17,38.25,65.25,24.3,706.66,5.78,7450.0,7455.0,7310.0,7530.0,7830.0,8000.0,7700.0,7420.0,7380.0,8400.0,8450.0,889.0,1156.0,864.0,1013.0,7460.0,7450.0,7360.0,7275.0,3596.28,1561.39,1460.98,7455.0,,11.75,11.15,1338.0,11.57,51.97,60.99,7590.0,7550.0,7620.0,7670.0,7730.0,7770.0,7850.0,7680.0,7800.0,7949.0,8250.0,8100.0,7640.0,8050.0,7690.0,8000.0,8050.0,8000.0,7900.0,8000.0,8100.0,7810.0,8300.0,7713.0,7800.0,7870.0,8000.0,7720.0,7770.0,7800.0,7750.0,8250.0,8050.0,7750.0,7800.0,8050.0,8080.0,8600.0,8100.0,8600.0,8050.0,8250.0,8300.0,7850.0,8400.0,8200.0,8530.0,8150.0,8300.0,7380.0,7900.0,7550.0,7650.0,7600.0,7700.0,7600.0,7800.0,7800.0,591.1,11200.0,9100.0,300.0,10500.0,-228.0673,-312.9785,2643.1641,2533.1641,-425.0,18.0,3726.8,-400.1084,1896.7634,-579.9908,-558.7632,-528.427,-500.3448,623.93,2823.8034,2661.5913,2587.0742,2568.8763,,2110.1385,-100.0,50.0,-711.0,650.0,550.0,690.0,130.7921,75.0,-10.0,-15.0,19.0,-4.0,-230.9375,-202.9785,-547.021,300.0,110.0,410.0,170.0,530.0,1030.0,870.0,945.0,995.0,18.0,1893.0,3089.541,15.0,690.0,455.0,0.1176,14.79,1.39,2.59,0.45,0.89,0.31,0.56,25.86,-0.1144,22.9,109.98,5.11,-0.7181,12.3,14.7,41.4,50.0,6.19,76.0,2564.0,12.54,12.33,1728.6,38.84,13.24,12.98,8.44,52.08,5.57,56.34,5.65,1327.9,4.8,43.64,36400.0,2820.0,0.42,,,776.355,88.9921,1.72,51.89,75.85,-4.48,68.5236,0.65,76.27,6.57,0.936,3.4218,2.127,62.75,69.91,75.7,79.7,31.74,96.78,81.67,66.9951,75.0457,1.2891,1.1562,0.9649,-17.33,-0.25,10.6625,-1.2376,23.18,602.0,-9.31,42.8372,15.312,48.9425,1.72,9.601,64.47,40.5429,0.2286,66.9951,75.2843,0.7972,0.0893,0.1074,-1.3824,0.0538,76.7763,9.7912,9.7265,24.75,5.25,7.39,5.36,3.39,0.71,3.67,9.32,6.66,0.0,5.76,0.85,0.16,0.0,2.05,2.23,-50.0,7.295,8.48,4.61,11.8975,2776.8,104.3,102.8429,31.08,1.2933,321.6,5.74,11.57,0.42,,9570.5,2749.2,3.61,104.282,2275.0,2487.0,6905.0,5378.0,625.13,7.129,3809.03,5418.0,8145.0,7455.0,7459.0,7440.0,7363.333333333333
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2024-11-01,-346.669,,,,-1055.0,7310.0,8600.0,9598.0,10500.0,51.0,-229.6843,33.7,44.07,22.22,4.81,0.6593,5750.0,553.84,425.0,1134.78,8.13,6.6,7.7,46.1,46.17,38.25,65.25,24.3,706.66,5.78,7450.0,7455.0,7310.0,7530.0,7830.0,8000.0,7700.0,7420.0,7380.0,8400.0,8450.0,889.0,1156.0,864.0,1013.0,7460.0,7450.0,7360.0,7275.0,3596.28,1561.39,1460.98,7455.0,,11.75,11.15,1338.0,11.57,51.97,60.99,7590.0,7550.0,7620.0,7670.0,7730.0,7770.0,7850.0,7680.0,7800.0,7949.0,8250.0,8100.0,7640.0,8050.0,7690.0,8000.0,8050.0,8000.0,7900.0,8000.0,8100.0,7810.0,8300.0,7713.0,7800.0,7870.0,8000.0,7720.0,7770.0,7800.0,7750.0,8250.0,8050.0,7750.0,7800.0,8050.0,8080.0,8600.0,8100.0,8600.0,8050.0,8250.0,8300.0,7850.0,8400.0,8200.0,8530.0,8150.0,8300.0,7380.0,7900.0,7550.0,7650.0,7600.0,7700.0,7600.0,7800.0,7800.0,591.1,11200.0,9100.0,300.0,10500.0,-228.0673,-312.9785,2643.1641,2533.1641,-425.0,18.0,3726.8,-400.1084,1896.7634,-579.9908,-558.7632,-528.427,-500.3448,623.93,2823.8034,2661.5913,2587.0742,2568.8763,,2110.1385,-100.0,50.0,-711.0,650.0,550.0,690.0,130.7921,75.0,-10.0,-15.0,19.0,-4.0,-230.9375,-202.9785,-547.021,300.0,110.0,410.0,170.0,530.0,1030.0,870.0,945.0,995.0,18.0,1893.0,3089.541,15.0,690.0,455.0,0.1176,14.79,1.39,2.59,0.45,0.89,0.31,0.56,25.86,-0.1144,22.9,109.98,5.11,-0.7181,12.3,14.7,41.4,50.0,6.19,76.0,2564.0,12.54,12.33,1728.6,38.84,13.24,12.98,8.44,52.08,5.57,56.34,5.65,1327.9,4.8,43.64,36400.0,2820.0,0.42,,,776.355,88.9921,1.72,51.89,75.85,-4.48,68.5236,0.65,76.27,6.57,0.936,3.4218,2.127,62.75,69.91,75.7,79.7,31.74,96.78,81.67,66.9951,75.0457,1.2891,1.1562,0.9649,-17.33,-0.25,10.6625,-1.2376,23.18,602.0,-9.31,42.8372,15.312,48.9425,1.72,9.601,64.47,40.5429,0.2286,66.9951,75.2843,0.7972,0.0893,0.1074,-1.3824,0.0538,76.7763,9.7912,9.7265,24.75,5.25,7.39,5.36,3.39,0.71,3.67,9.32,6.66,0.0,5.76,0.85,0.16,0.0,2.05,2.23,-50.0,7.295,8.48,4.61,11.8975,2776.8,104.3,102.8429,31.08,1.2933,321.6,5.74,11.57,0.42,,9570.5,2749.2,3.61,104.282,2275.0,2487.0,6905.0,5378.0,625.13,7.129,3809.03,5418.0,8145.0,7455.0,7459.0,7440.0,7363.333333333333
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2024-11-04,-347.669,,,,-1055.0,7290.0,8570.0,9598.0,10500.0,,-230.7186,,,,,,5750.0,560.28,425.0,,,,,,,,,,,,7450.0,7454.0,7290.0,7530.0,7830.0,8000.0,7700.0,7420.0,7380.0,8400.0,8450.0,889.0,1156.0,864.0,1013.0,7434.0,7450.0,7350.0,7275.0,3625.41,1568.38,1469.62,7454.0,1191.0,,,,,,,7600.0,7570.0,7620.0,7670.0,7730.0,7770.0,7850.0,7680.0,7800.0,7930.0,8250.0,8100.0,7610.0,8050.0,7660.0,8000.0,8050.0,8000.0,7900.0,8000.0,8100.0,7780.0,8300.0,7703.0,7800.0,7870.0,8000.0,7690.0,7740.0,7800.0,7750.0,8250.0,8050.0,7750.0,7800.0,8050.0,8080.0,8600.0,8100.0,8600.0,8050.0,8250.0,8300.0,7850.0,8400.0,8200.0,8480.0,8150.0,8300.0,7350.0,7900.0,7520.0,7700.0,7570.0,7700.0,7600.0,7800.0,7800.0,,11200.0,9100.0,230.0,10500.0,,-151.6966,,,-445.0,26.0,3730.7,-440.2676,1871.4791,-622.421,-584.4736,-569.4712,-542.775,624.4375,2773.0541,2608.97,2548.2167,2528.6829,407.0,2065.9202,-84.0,60.0,-654.0,695.0,529.0,711.0,,75.0,16.0,-11.0,24.0,-13.0,-212.0134,-41.6966,-451.488,300.0,110.0,410.0,170.0,530.0,1030.0,870.0,946.0,996.0,26.0,1953.2,2970.7144,-5.0,711.0,425.0,0.1746,,,,,,,,,,,,,,,,,46.875,,74.0,,,,,,,,,,,,,,,,,,,,,754.455,83.8754,,,75.525,,,-2.0,75.15,,0.999,,2.067,62.09,70.2,73.73,77.26,30.37,96.78,78.89,,,1.2498,,0.9724,,,10.5828,,,602.0,,,,,,9.6697,,,,66.9951,75.8229,0.4743,0.0521,-0.096,,,76.5547,9.763,9.5838,24.82,4.19,6.68,5.18,1.88,1.11,3.67,11.6,6.75,0.0,4.82,1.43,0.16,0.0,2.05,1.41,-50.0,,,,,,143.75,113.0857,,,,,11.57,,,9696.5,2746.2,3.61,103.885,2280.0,2468.0,6925.0,5333.0,632.6354,7.1009,3827.56,5478.0,8165.0,7454.0,7467.0,7443.0,7360.0
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2024-11-04,-347.669,,,,-1055.0,7290.0,8570.0,9598.0,10500.0,,-230.7186,,,,,,5750.0,560.28,425.0,,,,,,,,,,,,7450.0,7454.0,7290.0,7530.0,7830.0,8000.0,7700.0,7420.0,7380.0,8400.0,8450.0,889.0,1156.0,864.0,1013.0,7434.0,7450.0,7350.0,7275.0,3625.41,1568.38,1469.62,7454.0,1191.0,,,,,,,7600.0,7570.0,7620.0,7670.0,7730.0,7770.0,7850.0,7680.0,7800.0,7930.0,8250.0,8100.0,7610.0,8050.0,7660.0,8000.0,8050.0,8000.0,7900.0,8000.0,8100.0,7780.0,8300.0,7703.0,7800.0,7870.0,8000.0,7690.0,7740.0,7800.0,7750.0,8250.0,8050.0,7750.0,7800.0,8050.0,8080.0,8600.0,8100.0,8600.0,8050.0,8250.0,8300.0,7850.0,8400.0,8200.0,8480.0,8150.0,8300.0,7350.0,7900.0,7520.0,7700.0,7570.0,7700.0,7600.0,7800.0,7800.0,,11200.0,9100.0,230.0,10500.0,,-151.6966,,,-445.0,26.0,3730.7,-440.2676,1871.4791,-622.421,-584.4736,-569.4712,-542.775,624.4375,2773.0541,2608.97,2548.2167,2528.6829,407.0,2065.9202,-84.0,60.0,-654.0,695.0,529.0,711.0,,75.0,16.0,-11.0,24.0,-13.0,-212.0134,-41.6966,-451.488,300.0,110.0,410.0,170.0,530.0,1030.0,870.0,946.0,996.0,26.0,1953.2,2970.7144,-5.0,711.0,425.0,0.1746,,,,,,,,,,,,,,,,,46.875,,74.0,,,,,,,,,,,,,,,,,,,,,754.455,83.8754,,,75.525,,,-2.0,75.15,,0.999,,2.067,62.09,70.2,73.73,77.26,30.37,96.78,78.89,,,1.2498,,0.9724,,,10.5828,,,602.0,,,,,,9.6697,,,,66.9951,75.8229,0.4743,0.0521,-0.096,,,76.5547,9.763,9.5838,24.82,4.19,6.68,5.18,1.88,1.11,3.67,11.6,6.75,0.0,4.82,1.43,0.16,0.0,2.05,1.41,-50.0,,,,,,143.75,113.0857,,,,,11.57,,,9696.5,2746.2,3.61,103.885,2280.0,2468.0,6925.0,5333.0,632.6354,7.1009,3827.56,5478.0,8165.0,7454.0,7467.0,7443.0,7360.0
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2024-11-05,,,,,-643.0,,8580.0,9598.0,10500.0,,-234.6129,,,,,,5750.0,562.12,425.0,,,,,,,,,,,,7450.0,,,7530.0,7830.0,8000.0,7700.0,7420.0,7400.0,8400.0,8450.0,889.0,1156.0,864.0,1013.0,7469.0,7475.0,7360.0,7280.0,3637.92,1568.34,1467.34,7472.0,,,,,,,,7600.0,7570.0,7620.0,7670.0,7730.0,7770.0,7850.0,7680.0,7800.0,7930.0,8250.0,8100.0,7630.0,8050.0,7680.0,8000.0,8050.0,8000.0,7900.0,8000.0,8150.0,7800.0,8300.0,7693.0,7800.0,7870.0,8000.0,7710.0,7760.0,7800.0,7750.0,8250.0,8050.0,7700.0,7800.0,8050.0,8080.0,8600.0,8100.0,8600.0,8050.0,8250.0,8300.0,7850.0,8400.0,8200.0,8450.0,8150.0,8300.0,7390.0,7900.0,7540.0,7700.0,7590.0,7700.0,7600.0,7800.0,7800.0,,11200.0,9100.0,220.0,10500.0,-177.3596,,,,-435.0,,,-513.4425,1798.9656,-698.1214,-657.7635,,,,,,,,,,-109.0,,-680.0,640.0,,,37.7042,200.0,6.0,,,,,,,300.0,110.0,410.0,170.0,530.0,1030.0,870.0,946.0,,,,,-15.0,,425.0,0.0677,,,,,,,,,,,,,,,,,42.1875,,71.0,,,,,,,,,,,,,,,,,,,,,,80.8276,,,74.815,,,-2.58,74.48,,1.0,,,56.75,70.2,73.73,77.26,26.94,96.78,78.89,,,1.2503,,0.977,,,10.4988,,,602.0,,,,,,9.6309,,,,66.9951,75.5186,0.3994,0.0439,-0.085,,,76.4323,9.7474,9.4988,24.3,5.01,7.6,4.47,1.88,1.11,4.61,10.89,5.81,0.0,3.76,1.43,0.4,0.0,2.05,1.41,-75.0,,,,,,97.0,110.8429,,,,,11.57,,,9738.5,2749.7,3.54,103.423,2280.0,2485.0,6935.0,,640.5259,7.1047,3850.18,5442.0,,,,,7357.5
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2024-11-05,-329.669,,,,-643.0,7290.0,8580.0,9598.0,10500.0,,-234.6129,,,,,,5750.0,562.12,425.0,,,,,,,,,,,,7450.0,7472.0,7290.0,7530.0,7830.0,8000.0,7700.0,7420.0,7400.0,8400.0,8450.0,889.0,1156.0,864.0,1013.0,7469.0,7475.0,7360.0,7280.0,3637.92,1568.34,1467.34,7472.0,,,,,,,,7600.0,7570.0,7620.0,7670.0,7730.0,7770.0,7850.0,7680.0,7800.0,7930.0,8250.0,8100.0,7630.0,8050.0,7680.0,8000.0,8050.0,8000.0,7900.0,8000.0,8150.0,7800.0,8300.0,7693.0,7800.0,7870.0,8000.0,7710.0,7760.0,7800.0,7750.0,8250.0,8050.0,7700.0,7800.0,8050.0,8080.0,8600.0,8100.0,8600.0,8050.0,8250.0,8300.0,7850.0,8400.0,8200.0,8450.0,8150.0,8300.0,7390.0,7900.0,7540.0,7700.0,7590.0,7700.0,7600.0,7800.0,7800.0,,11200.0,9100.0,220.0,10500.0,-177.3596,,,,-435.0,-31.0,3720.0,-513.4425,1798.9656,-698.1214,-657.7635,-626.7169,-602.5462,625.05,2746.3243,2578.0471,2503.692,2476.2981,,2007.4173,-109.0,70.0,-680.0,640.0,537.0,726.0,37.7042,200.0,6.0,-10.0,22.0,-12.0,,,,300.0,110.0,410.0,170.0,530.0,1030.0,870.0,928.0,978.0,-31.0,1959.2,2960.7663,-15.0,726.0,425.0,0.0677,,,,,,,,,,,,,,,,,42.1875,,71.0,,,,,,,,,,,,,,,,,,,,,,80.8276,,,74.815,,,-2.58,74.48,,1.0,,,56.75,70.2,73.73,77.26,26.94,96.78,78.89,,,1.2503,,0.977,,,10.4988,,,602.0,,,,,,9.6309,,,,66.9951,75.5186,0.3994,0.0439,-0.085,,,76.4323,9.7474,9.4988,24.3,5.01,7.6,4.47,1.88,1.11,4.61,10.89,5.81,0.0,3.76,1.43,0.4,0.0,2.05,1.41,-75.0,,,,,,97.0,110.8429,,,,,11.57,,,9738.5,2749.7,3.54,103.423,2280.0,2485.0,6935.0,5341.0,640.5259,7.1047,3850.18,5442.0,8198.0,7472.0,7484.0,7462.0,7357.5
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2024-11-06,,,,,-628.0,,8600.0,9598.0,10500.0,,-248.1257,,,,,,5750.0,560.28,425.0,,,,,,,,,,,,7450.0,,,7530.0,7680.0,8000.0,7700.0,7430.0,7400.0,8400.0,8450.0,889.0,1142.0,864.0,1068.0,7489.0,7490.0,7360.0,7320.0,3587.73,1550.2,1457.82,7427.0,,,,,,,,7600.0,7590.0,7620.0,7700.0,7760.0,7790.0,7850.0,7680.0,7850.0,7940.0,8250.0,8100.0,7630.0,8050.0,7680.0,8000.0,8050.0,8000.0,7900.0,8000.0,8150.0,7800.0,8300.0,7713.0,7800.0,7870.0,8000.0,7830.0,7880.0,7800.0,7750.0,8250.0,8050.0,7700.0,7800.0,8050.0,8080.0,8600.0,8100.0,8600.0,8050.0,8250.0,8300.0,7850.0,8400.0,8200.0,8350.0,8150.0,8300.0,7390.0,7900.0,7540.0,7700.0,7590.0,7700.0,7600.0,7800.0,7800.0,,11200.0,9100.0,200.0,10500.0,-179.6001,-266.3958,,,-440.0,,,-502.462,1806.3686,-695.9168,-650.3379,,,,,,,,,,-129.0,,-650.0,680.0,,,24.9439,215.0,1.0,,,,-207.4516,-166.3958,-622.9745,150.0,100.0,250.0,320.0,520.0,1020.0,870.0,946.0,,,,,-10.0,,420.0,0.0226,,,,,,,,,,,,,,,,,37.5,,68.0,,,,,,,,,,,,,,,,,,,,,851.91,76.2021,,,73.975,,,-4.62,73.47,,1.04,,2.334,59.19,67.69,73.73,77.26,26.94,92.64,77.78,,,1.2279,,0.9899,,,10.4098,,,602.0,,,,,,9.5923,,,,66.9951,75.2157,0.1492,0.0162,-0.129,,,76.2643,9.726,9.3698,25.64,6.92,6.35,4.47,1.88,0.71,3.9,7.32,5.74,0.0,5.79,1.43,0.4,0.0,2.05,1.41,-90.0,,,,,,,,,,,,11.57,,,9343.0,2676.3,3.23,105.088,2300.0,2480.0,6940.0,,635.0807,7.1753,3815.89,5391.0,,,,,7383.333333333333
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2024-11-06,-374.669,,,,-628.0,7290.0,8600.0,9598.0,10500.0,,-248.1257,,,,,,5750.0,560.28,425.0,,,,,,,,,,,,7450.0,7427.0,7290.0,7530.0,7680.0,8000.0,7700.0,7430.0,7400.0,8400.0,8450.0,889.0,1142.0,864.0,1068.0,7489.0,7490.0,7360.0,7320.0,3587.73,1550.2,1457.82,7427.0,,,,,,,,7600.0,7590.0,7620.0,7700.0,7760.0,7790.0,7850.0,7680.0,7850.0,7940.0,8250.0,8100.0,7630.0,8050.0,7680.0,8000.0,8050.0,8000.0,7900.0,8000.0,8150.0,7800.0,8300.0,7713.0,7800.0,7870.0,8000.0,7830.0,7880.0,7800.0,7750.0,8250.0,8050.0,7700.0,7800.0,8050.0,8080.0,8600.0,8100.0,8600.0,8050.0,8250.0,8300.0,7850.0,8400.0,8200.0,8350.0,8150.0,8300.0,7390.0,7900.0,7540.0,7700.0,7590.0,7700.0,7600.0,7800.0,7800.0,,11200.0,9100.0,200.0,10500.0,-179.6001,-266.3958,,,-440.0,-10.0,3698.1,-502.462,1806.3686,-695.9168,-650.3379,-664.4089,-640.1646,625.0325,2706.3989,2540.7543,2465.8198,2434.2685,,1966.6524,-129.0,70.0,-650.0,680.0,487.0,760.0,24.9439,215.0,1.0,4.0,12.0,-16.0,-207.4516,-166.3958,-622.9745,150.0,100.0,250.0,320.0,520.0,1020.0,870.0,973.0,1023.0,-10.0,2028.2,2903.4834,-10.0,760.0,420.0,0.0226,,,,,,,,,,,,,,,,,37.5,,68.0,,,,,,,,,,,,,,,,,,,,,851.91,76.2021,,,73.975,,,-4.62,73.47,,1.04,,2.334,59.19,67.69,73.73,77.26,26.94,92.64,77.78,,,1.2279,,0.9899,,,10.4098,,,602.0,,,,,,9.5923,,,,66.9951,75.2157,0.1492,0.0162,-0.129,,,76.2643,9.726,9.3698,25.64,6.92,6.35,4.47,1.88,0.71,3.9,7.32,5.74,0.0,5.79,1.43,0.4,0.0,2.05,1.41,-90.0,,,,,,,,,,,,11.57,,,9343.0,2676.3,3.23,105.088,2300.0,2480.0,6940.0,5338.0,635.0807,7.1753,3815.89,5391.0,8187.0,7427.0,7443.0,7431.0,7383.333333333333
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2024-11-07,,,,,-653.0,,8600.0,9598.0,10500.0,,-256.4857,,,,,,5750.0,572.24,425.0,,,,,,,,,,,,7450.0,,,7530.0,7680.0,8000.0,7700.0,7430.0,7400.0,8400.0,8450.0,889.0,1142.0,864.0,1068.0,7453.0,7465.0,7360.0,7320.0,3644.65,1570.08,1474.42,7499.0,,,,,,,,7600.0,7590.0,7650.0,7700.0,7760.0,7790.0,7850.0,7680.0,7850.0,7936.0,8250.0,8100.0,7610.0,8050.0,7660.0,8050.0,8050.0,8000.0,7900.0,8000.0,8150.0,7780.0,,7723.0,7800.0,7870.0,8000.0,7810.0,7860.0,7800.0,7750.0,8250.0,8050.0,7700.0,7800.0,8050.0,8080.0,8600.0,8100.0,8600.0,8050.0,8250.0,8300.0,7850.0,8350.0,8200.0,8350.0,8150.0,8300.0,7380.0,7900.0,7520.0,7700.0,7570.0,7700.0,7600.0,7800.0,7800.0,,11200.0,9100.0,200.0,10300.0,-161.6263,-267.2611,,,-440.0,,,-488.9947,1821.5546,-681.9848,-635.0091,,,,,,,,,,-93.0,,-681.0,695.0,,,71.3912,190.0,12.0,,,,-203.4326,-167.2611,-683.9914,150.0,100.0,250.0,320.0,520.0,1020.0,870.0,946.0,,,,,-10.0,,420.0,0.0229,,,,,,,,,,,,,,,,,35.9375,,67.0,,,,,,,,,,,,,,,,,,,,,867.24,76.2379,,,73.475,,,-4.44,73.48,,1.028,,2.376,59.19,67.87,73.73,77.26,19.63,93.93,77.78,,,1.2376,,1.0001,,,10.3988,,,602.0,,,,,,9.5569,,,,66.9951,74.9371,0.1696,0.0184,0.001,,,76.1207,9.7077,9.3708,26.49,6.77,6.35,4.47,2.59,0.24,4.49,7.1,5.03,0.0,5.43,1.43,0.4,0.0,1.46,1.41,-65.0,,,,,,,,,,,,11.57,,,9664.0,2705.8,3.27,104.508,2310.0,2482.0,6940.0,,633.86,7.1434,3871.41,5461.0,,,,,7366.666666666667
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2024-11-07,-302.669,,,,-653.0,7290.0,8600.0,9598.0,10500.0,,-256.4857,,,,,,5750.0,572.24,425.0,,,,,,,,,,,,7450.0,7499.0,7290.0,7530.0,7680.0,8000.0,7700.0,7430.0,7400.0,8400.0,8450.0,889.0,1142.0,864.0,1068.0,7453.0,7465.0,7360.0,7320.0,3644.65,1570.08,1474.42,7499.0,,,,,,,,7600.0,7590.0,7650.0,7700.0,7760.0,7790.0,7850.0,7680.0,7850.0,7936.0,8250.0,8100.0,7610.0,8050.0,7660.0,8050.0,8050.0,8000.0,7900.0,8000.0,8150.0,7780.0,,7723.0,7800.0,7870.0,8000.0,7810.0,7860.0,7800.0,7750.0,8250.0,8050.0,7700.0,7800.0,8050.0,8080.0,8600.0,8100.0,8600.0,8050.0,8250.0,8300.0,7850.0,8350.0,8200.0,8350.0,8150.0,8300.0,7380.0,7900.0,7520.0,7700.0,7570.0,7700.0,7600.0,7800.0,7800.0,,11200.0,9100.0,200.0,10300.0,-161.6263,-267.2611,,,-440.0,-31.0,3700.1,-488.9947,1821.5546,-681.9848,-635.0091,-587.2248,-562.5157,625.4775,2783.256,2619.7673,2556.8667,2519.1831,,2052.6467,-93.0,70.0,-681.0,695.0,559.0,771.0,71.3912,190.0,12.0,-14.0,16.0,-2.0,-203.4326,-167.2611,-683.9914,150.0,100.0,250.0,320.0,520.0,1020.0,870.0,901.0,951.0,-31.0,2046.2,2953.7275,-10.0,771.0,420.0,0.0229,,,,,,,,,,,,,,,,,35.9375,,67.0,,,,,,,,,,,,,,,,,,,,,867.24,76.2379,,,73.475,,,-4.44,73.48,,1.028,,2.376,59.19,67.87,73.73,77.26,19.63,93.93,77.78,,,1.2376,,1.0001,,,10.3988,,,602.0,,,,,,9.5569,,,,66.9951,74.9371,0.1696,0.0184,0.001,,,76.1207,9.7077,9.3708,26.49,6.77,6.35,4.47,2.59,0.24,4.49,7.1,5.03,0.0,5.43,1.43,0.4,0.0,1.46,1.41,-65.0,,,,,,,,,,,,11.57,,,9664.0,2705.8,3.27,104.508,2310.0,2482.0,6940.0,5336.0,633.86,7.1434,3871.41,5461.0,8270.0,7499.0,7501.0,7485.0,7366.666666666667
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2024-11-08,,,,,-633.0,,8630.0,9629.0,10520.0,52.9,-260.86,25.49,54.12,20.39,-8.21,0.7999,5750.0,555.68,445.0,1238.67,8.41,6.88,7.82,46.8,46.97,39.38,65.25,23.93,789.82,5.78,7450.0,,,7530.0,7680.0,8000.0,7750.0,7430.0,7400.0,8400.0,8450.0,889.0,1142.0,864.0,1068.0,7457.0,7485.0,7360.0,7280.0,3597.72,1560.08,1462.45,7462.0,,11.47,11.01,1321.2,11.17,52.47,86.18,7600.0,7610.0,7650.0,,7790.0,7830.0,7850.0,7700.0,7900.0,7961.0,8250.0,8100.0,7660.0,8050.0,7710.0,8050.0,8050.0,8000.0,7900.0,8050.0,8250.0,7830.0,,7733.0,7800.0,7870.0,8000.0,7860.0,7910.0,7800.0,7750.0,8250.0,8050.0,7700.0,7800.0,8050.0,8080.0,8600.0,8100.0,8600.0,8050.0,8250.0,8350.0,7850.0,8350.0,8200.0,8450.0,8150.0,8300.0,7400.0,7950.0,7570.0,7700.0,7620.0,7700.0,7650.0,7900.0,7900.0,602.0,11200.0,9100.0,270.0,10300.0,-117.9361,-233.7372,,,-430.0,,,-455.3133,1849.5344,-647.1409,-606.6719,,,,,,,,,,-97.0,,-685.0,625.0,,,135.6337,210.0,28.0,,,,-196.4631,-133.7372,-632.6395,150.0,100.0,250.0,320.0,520.0,1020.0,870.0,946.0,,,,,0.0,,430.0,0.0234,16.09,1.35,2.58,0.48,0.83,0.32,0.56,27.23,-0.1629,22.48,109.3218,5.18,0.1148,,,,33.5938,6.12,65.5,-582.0,12.18,12.1825,1802.97,39.62,13.89,12.39,8.67,53.51,5.51,56.82,5.69,1350.6025,5.22,44.84,35818.0,2752.8,0.16,,,867.24,77.2778,0.47,52.22,73.595,-3.6,67.8726,-4.37,73.71,6.49,1.028,3.0028,2.376,56.6,67.87,73.01,77.45,19.63,97.24,80.56,66.898,74.9371,1.246,1.1606,1.0133,-14.03,1.13,10.4278,-0.7019,,571.0,,41.7572,15.093,49.6,0.47,9.5102,65.1525,41.31,0.2256,66.898,74.5714,0.1796,0.0195,0.029,-0.0971,0.047,75.9857,9.6904,9.3998,25.9,6.89,7.06,6.0,2.59,0.24,5.81,8.28,3.74,0.0,3.41,0.73,0.4,0.0,2.16,1.41,-85.0,7.34,8.4233,4.14,11.5575,2680.8,,,,0.929,358.8,5.69,11.17,0.16,,9443.5,2694.8,3.49,104.997,2310.0,2490.0,6930.0,,629.3241,7.1841,3848.42,5393.0,,,,,7383.333333333333
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2024-11-08,-339.669,,,,-633.0,7300.0,8630.0,9629.0,10520.0,52.9,-260.86,25.49,54.12,20.39,-8.21,0.7999,5750.0,555.68,445.0,1238.67,8.41,6.88,7.82,46.8,46.97,39.38,65.25,23.93,789.82,5.78,7450.0,7462.0,7300.0,7530.0,7680.0,8000.0,7750.0,7430.0,7400.0,8400.0,8450.0,889.0,1142.0,864.0,1068.0,7457.0,7485.0,7360.0,7280.0,3597.72,1560.08,1462.45,7462.0,,11.47,11.01,1321.2,11.17,52.47,86.18,7600.0,7610.0,7650.0,,7790.0,7830.0,7850.0,7700.0,7900.0,7961.0,8250.0,8100.0,7660.0,8050.0,7710.0,8050.0,8050.0,8000.0,7900.0,8050.0,8250.0,7830.0,,7733.0,7800.0,7870.0,8000.0,7860.0,7910.0,7800.0,7750.0,8250.0,8050.0,7700.0,7800.0,8050.0,8080.0,8600.0,8100.0,8600.0,8050.0,8250.0,8350.0,7850.0,8350.0,8200.0,8450.0,8150.0,8300.0,7400.0,7950.0,7570.0,7700.0,7620.0,7700.0,7650.0,7900.0,7900.0,602.0,11200.0,9100.0,270.0,10300.0,-117.9361,-233.7372,,,-430.0,-41.0,3695.3,-455.3133,1849.5344,-647.1409,-606.6719,-586.2867,-560.4152,626.13,2760.9549,2599.2565,2536.1546,2514.7465,,2051.2224,-97.0,60.0,-685.0,625.0,532.0,782.0,135.6337,210.0,28.0,5.0,5.0,-10.0,-196.4631,-133.7372,-632.6395,150.0,100.0,250.0,320.0,520.0,1020.0,870.0,938.0,988.0,-41.0,2009.2,3014.3788,0.0,782.0,430.0,0.0234,16.09,1.35,2.58,0.48,0.83,0.32,0.56,27.23,-0.1629,22.48,109.3218,5.18,0.1148,,,,33.5938,6.12,65.5,-582.0,12.18,12.1825,1802.97,39.62,13.89,12.39,8.67,53.51,5.51,56.82,5.69,1350.6025,5.22,44.84,35818.0,2752.8,0.16,,,867.24,77.2778,0.47,52.22,73.595,-3.6,67.8726,-4.37,73.71,6.49,1.028,3.0028,2.376,56.6,67.87,73.01,77.45,19.63,97.24,80.56,66.898,74.9371,1.246,1.1606,1.0133,-14.03,1.13,10.4278,-0.7019,,571.0,,41.7572,15.093,49.6,0.47,9.5102,65.1525,41.31,0.2256,66.898,74.5714,0.1796,0.0195,0.029,-0.0971,0.047,75.9857,9.6904,9.3998,25.9,6.89,7.06,6.0,2.59,0.24,5.81,8.28,3.74,0.0,3.41,0.73,0.4,0.0,2.16,1.41,-85.0,7.34,8.4233,4.14,11.5575,2680.8,,,,0.929,358.8,5.69,11.17,0.16,,9443.5,2694.8,3.49,104.997,2310.0,2490.0,6930.0,5322.0,629.3241,7.1841,3848.42,5393.0,8244.0,7462.0,7472.0,7467.0,7383.333333333333
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2024-11-11,,,,,-633.0,,8580.0,9594.0,10520.0,,-259.41,,,,,,5750.0,544.64,445.0,,,,,,,,,,,,7475.0,,,7480.0,7680.0,8000.0,7750.0,7430.0,7400.0,8400.0,8400.0,889.0,1142.0,864.0,1068.0,7501.0,7485.0,7350.0,7280.0,3555.0,1547.44,1451.56,7435.0,1190.0,,,,,,,7600.0,7610.0,7650.0,7700.0,7760.0,,7850.0,7700.0,7900.0,7959.0,8250.0,8100.0,7630.0,8050.0,7680.0,8050.0,8100.0,8000.0,7900.0,8050.0,8150.0,7800.0,,7653.0,7800.0,7850.0,8000.0,7830.0,7880.0,7800.0,7750.0,8250.0,8050.0,7700.0,7800.0,8050.0,8080.0,8600.0,8100.0,8600.0,8050.0,8250.0,8350.0,7850.0,8400.0,8200.0,8450.0,8250.0,8310.0,7370.0,7950.0,7540.0,7700.0,7590.0,7720.0,7650.0,7900.0,7900.0,,11200.0,9100.0,320.0,10300.0,-65.6154,-272.9596,,,-390.0,,,-420.0085,1849.3447,-610.6177,-606.487,,,,,,,,407.0,,-151.0,,-610.0,655.0,,,249.439,210.0,-16.0,,,,-199.1127,-222.9596,-695.618,200.0,50.0,250.0,320.0,520.0,970.0,920.0,946.0,,,,,40.0,,460.0,0.1339,,,,,,,,,,,,,,,,,43.75,,72.0,,,,,,,,,,,,,,,,,,,,,885.855,80.7845,,,73.92,,,-2.0,73.87,,0.985,,2.427,56.6,72.09,72.9,75.18,31.28,93.75,83.33,,,1.2136,,1.0131,,,10.4826,,,606.0,,,,,,9.4596,,,,66.898,73.9157,0.5393,0.0602,-0.0132,,,75.7273,9.6653,9.4976,25.78,6.45,8.8,5.18,3.46,0.0,4.66,7.01,3.03,0.0,3.69,0.73,0.64,0.47,1.88,1.04,-85.0,,,,,,,,,,,,11.17,,,9331.5,2617.7,3.79,105.543,2310.0,2465.0,6890.0,,622.72,7.215,3806.98,5335.0,,,,,7340.0
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2024-11-11,-366.669,,,,-633.0,7270.0,8580.0,9594.0,10520.0,,-259.41,,,,,,5750.0,544.64,445.0,,,,,,,,,,,,7475.0,7435.0,7270.0,7480.0,7680.0,8000.0,7750.0,7430.0,7400.0,8400.0,8400.0,889.0,1142.0,864.0,1068.0,7501.0,7485.0,7350.0,7280.0,3555.0,1547.44,1451.56,7435.0,1190.0,,,,,,,7600.0,7610.0,7650.0,7700.0,7760.0,,7850.0,7700.0,7900.0,7959.0,8250.0,8100.0,7630.0,8050.0,7680.0,8050.0,8100.0,8000.0,7900.0,8050.0,8150.0,7800.0,,7653.0,7800.0,7850.0,8000.0,7830.0,7880.0,7800.0,7750.0,8250.0,8050.0,7700.0,7800.0,8050.0,8080.0,8600.0,8100.0,8600.0,8050.0,8250.0,8350.0,7850.0,8400.0,8200.0,8450.0,8250.0,8310.0,7370.0,7950.0,7540.0,7700.0,7590.0,7720.0,7650.0,7900.0,7900.0,,11200.0,9100.0,320.0,10300.0,-65.6154,-272.9596,,,-390.0,-26.0,3701.9,-420.0085,1849.3447,-610.6177,-606.487,-574.8758,-547.7858,627.1025,2791.3383,2597.5157,2561.3179,2522.216,407.0,2062.2466,-151.0,80.0,-610.0,655.0,545.0,766.0,249.439,210.0,-16.0,-8.0,8.0,0.0,-199.1127,-222.9596,-695.618,200.0,50.0,250.0,320.0,520.0,970.0,920.0,965.0,965.0,-26.0,1993.0,3092.588,40.0,766.0,460.0,0.1339,,,,,,,,,,,,,,,,,43.75,,72.0,,,,,,,,,,,,,,,,,,,,,885.855,80.7845,,,73.92,,,-2.0,73.87,,0.985,,2.427,56.6,72.09,72.9,75.18,31.28,93.75,83.33,,,1.2136,,1.0131,,,10.4826,,,606.0,,,,,,9.4596,,,,66.898,73.9157,0.5393,0.0602,-0.0132,,,75.7273,9.6653,9.4976,25.78,6.45,8.8,5.18,3.46,0.0,4.66,7.01,3.03,0.0,3.69,0.73,0.64,0.47,1.88,1.04,-85.0,,,,,,,,,,,,11.17,,,9331.5,2617.7,3.79,105.543,2310.0,2465.0,6890.0,5328.0,622.72,7.215,3806.98,5335.0,8201.0,7435.0,7435.0,7427.0,7340.0
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2024-11-12,,,,,-1168.0,,8600.0,9597.0,10520.0,,-260.1071,,,,,,5750.0,547.4,445.0,,,,,,,,,,,,7450.0,,,7500.0,7680.0,8000.0,7750.0,7400.0,7380.0,8400.0,8400.0,889.0,1142.0,864.0,1068.0,7500.0,7460.0,7350.0,7220.0,3555.24,1547.19,1455.64,7441.0,,,,,,,,7650.0,7610.0,7670.0,7700.0,7760.0,7800.0,7850.0,7720.0,7900.0,7947.0,8250.0,8100.0,7630.0,8050.0,7680.0,8050.0,8100.0,8000.0,7900.0,8050.0,8150.0,7800.0,,7663.0,7800.0,7840.0,8000.0,7830.0,7880.0,7800.0,7750.0,8250.0,8050.0,7700.0,7800.0,8050.0,8080.0,8600.0,8100.0,8600.0,8050.0,8250.0,8350.0,7850.0,8400.0,8200.0,8500.0,8250.0,8310.0,7370.0,7950.0,7540.0,7700.0,7590.0,7720.0,7650.0,7900.0,7900.0,,11200.0,9100.0,300.0,10300.0,-228.9976,-304.6764,,,-370.0,,,-478.7709,1773.0199,-643.9431,-683.1535,,,,,,,,,,-150.0,,-644.0,565.0,,,172.6636,185.0,-40.0,,,,-199.6691,-204.6764,-667.3894,180.0,100.0,280.0,320.0,550.0,1000.0,900.0,946.0,,,,,50.0,,500.0,0.037,,,,,,,,,,,,,,,,,40.625,,70.0,,,,,,,,,,,,,,,,,,,,,853.005,87.6152,,,74.61,,,-1.82,75.35,,0.985,,2.337,61.94,70.76,75.05,75.61,31.28,97.66,83.33,,,1.2351,,1.011,,,10.6731,,,603.0,,,,,,9.4867,,,,66.898,74.04,0.5758,0.0632,0.1905,,,75.6737,9.661,9.6881,24.14,6.45,8.21,6.7,4.75,0.0,3.72,7.72,2.56,0.0,3.69,0.14,0.64,0.47,1.88,1.98,-60.0,,,,,,,,,,,,11.17,,,9142.0,2606.3,3.77,106.024,2310.0,2468.0,6850.0,,622.3,7.2347,3773.96,5350.0,,,,,7306.666666666667
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2024-11-12,-360.669,,,,-1168.0,7270.0,8600.0,9597.0,10520.0,,-260.1071,,,,,,5750.0,547.4,445.0,,,,,,,,,,,,7450.0,7441.0,7270.0,7500.0,7680.0,8000.0,7750.0,7400.0,7380.0,8400.0,8400.0,889.0,1142.0,864.0,1068.0,7500.0,7460.0,7350.0,7220.0,3555.24,1547.19,1455.64,7441.0,,,,,,,,7650.0,7610.0,7670.0,7700.0,7760.0,7800.0,7850.0,7720.0,7900.0,7947.0,8250.0,8100.0,7630.0,8050.0,7680.0,8050.0,8100.0,8000.0,7900.0,8050.0,8150.0,7800.0,,7663.0,7800.0,7840.0,8000.0,7830.0,7880.0,7800.0,7750.0,8250.0,8050.0,7700.0,7800.0,8050.0,8080.0,8600.0,8100.0,8600.0,8050.0,8250.0,8350.0,7850.0,8400.0,8200.0,8500.0,8250.0,8310.0,7370.0,7950.0,7540.0,7700.0,7590.0,7720.0,7650.0,7900.0,7900.0,,11200.0,9100.0,300.0,10300.0,-228.9976,-304.6764,,,-370.0,4.0,3790.5,-439.8433,1816.9155,-603.672,-638.8449,-562.8521,-535.5304,627.705,2772.8329,2658.9888,2579.3078,2535.4833,,2077.0302,-150.0,80.0,-644.0,565.0,591.0,801.0,172.6636,185.0,-40.0,-2.0,-9.0,11.0,-199.6691,-204.6764,-667.3894,180.0,100.0,280.0,320.0,550.0,1000.0,900.0,959.0,959.0,4.0,2011.0,3076.346,50.0,801.0,500.0,0.037,,,,,,,,,,,,,,,,,40.625,,70.0,,,,,,,,,,,,,,,,,,,,,853.005,87.6152,,,74.61,,,-1.82,75.35,,0.985,,2.337,61.94,70.76,75.05,75.61,31.28,97.66,83.33,,,1.2351,,1.011,,,10.6731,,,603.0,,,,,,9.4867,,,,66.898,74.04,0.5758,0.0632,0.1905,,,75.6737,9.661,9.6881,24.14,6.45,8.21,6.7,4.75,0.0,3.72,7.72,2.56,0.0,3.69,0.14,0.64,0.47,1.88,1.98,-60.0,,,,,,,,,,,,11.17,,,9142.0,2606.3,3.77,106.024,2310.0,2468.0,6850.0,5325.0,617.5875,7.2347,3773.96,5350.0,8242.0,7441.0,7430.0,7439.0,7306.666666666667
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2024-11-13,,,,,-1153.0,,8600.0,9597.0,10520.0,,-264.54,,,,,,5750.0,543.72,465.0,,,,,,,,,,,,7450.0,,,7500.0,7680.0,8000.0,7750.0,7400.0,7380.0,8400.0,8400.0,889.0,1125.0,864.0,1101.0,7456.0,7475.0,7350.0,7260.0,3556.8,1549.98,1455.12,7441.0,,,,,,,,7670.0,7630.0,7670.0,7720.0,7780.0,7810.0,7850.0,7720.0,7900.0,7959.0,8250.0,8100.0,7650.0,8050.0,7700.0,8050.0,8100.0,8000.0,7900.0,8050.0,8150.0,7820.0,,7623.0,7800.0,7800.0,8000.0,7850.0,7900.0,7800.0,7750.0,8250.0,8050.0,7700.0,7800.0,8050.0,8080.0,8600.0,8100.0,8600.0,8050.0,8250.0,8350.0,7850.0,8400.0,8200.0,8500.0,8250.0,8310.0,7390.0,7950.0,7560.0,7700.0,7610.0,7720.0,7650.0,7900.0,7900.0,,11200.0,9100.0,300.0,10300.0,-191.7318,-270.9322,,,-320.0,,,-465.9272,1737.5027,-630.6562,-718.5344,,,,,,,,,,-106.0,,-665.0,590.0,,,228.3425,200.0,19.0,,,,-196.7954,-170.9322,-642.3305,180.0,100.0,280.0,320.0,550.0,1000.0,900.0,946.0,,,,,100.0,,550.0,0.0074,,,,,,,,,,,,,,,,,37.5,,68.0,,,,,,,,,,,,,,,,,,,,,896.805,80.7343,,,74.6,,,-4.21,73.85,,0.947,,2.457,61.81,71.99,74.87,68.73,30.59,97.52,83.33,,,1.206,,0.9977,,,10.4432,,,603.0,,,,,,9.5047,,,,66.898,74.0943,0.279,0.0303,-0.1919,,,75.533,9.6457,9.4962,21.61,6.02,8.03,5.72,4.71,0.7,3.71,8.59,3.24,0.0,4.6,0.14,0.4,0.7,1.4,3.13,-75.0,,,,,,,,,,,,11.17,,,9047.0,2586.5,3.85,106.481,2310.0,2480.0,6800.0,,621.88,7.234,3772.43,5347.0,,,,,7323.333333333333
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2024-11-13,-360.669,,,,-1153.0,7270.0,8600.0,9597.0,10520.0,,-264.54,,,,,,5750.0,543.72,465.0,,,,,,,,,,,,7450.0,7441.0,7270.0,7500.0,7680.0,8000.0,7750.0,7400.0,7380.0,8400.0,8400.0,889.0,1125.0,864.0,1101.0,7456.0,7475.0,7350.0,7260.0,3556.8,1549.98,1455.12,7441.0,,,,,,,,7670.0,7630.0,7670.0,7720.0,7780.0,7810.0,7850.0,7720.0,7900.0,7959.0,8250.0,8100.0,7650.0,8050.0,7700.0,8050.0,8100.0,8000.0,7900.0,8050.0,8150.0,7820.0,,7623.0,7800.0,7800.0,8000.0,7850.0,7900.0,7800.0,7750.0,8250.0,8050.0,7700.0,7800.0,8050.0,8080.0,8600.0,8100.0,8600.0,8050.0,8250.0,8350.0,7850.0,8400.0,8200.0,8500.0,8250.0,8310.0,7390.0,7950.0,7560.0,7700.0,7610.0,7720.0,7650.0,7900.0,7900.0,,11200.0,9100.0,300.0,10300.0,-191.7318,-270.9322,,,-320.0,-35.0,3764.6,-407.6131,1737.5027,-630.6562,-718.5344,-588.936,-562.5146,628.3375,2772.604,2621.4738,2545.9087,2510.1147,,2049.1511,-106.0,80.0,-665.0,590.0,641.0,798.0,228.3425,200.0,19.0,4.0,-1.0,-3.0,-196.7954,-170.9322,-642.3305,180.0,100.0,280.0,320.0,550.0,1000.0,900.0,959.0,959.0,-35.0,2012.2,3069.3783,100.0,798.0,550.0,0.0074,,,,,,,,,,,,,,,,,37.5,,68.0,,,,,,,,,,,,,,,,,,,,,896.805,80.7343,,,74.6,,,-4.21,73.85,,0.947,,2.457,61.81,71.99,74.87,68.73,30.59,97.52,83.33,,,1.2554,,0.9977,,,10.4432,,,603.0,,,,,,9.5047,,,,66.898,74.0943,0.279,0.0303,-0.1919,,,75.533,9.6457,9.4962,21.61,6.02,8.03,5.72,4.71,0.7,3.71,8.59,3.24,0.0,4.6,0.14,0.4,0.7,1.4,3.13,-75.0,,,,,,,,,,,,11.17,,,9047.0,2586.5,3.85,106.481,2310.0,2480.0,6800.0,5324.0,621.88,7.234,3772.43,5347.0,8239.0,7441.0,7444.0,7445.0,7323.333333333333
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2024-11-14,,,,,-1193.0,,8600.0,9624.5,10520.0,,-260.6829,,,,,,5750.0,545.56,465.0,,,,,,,,,,,,7475.0,,,7500.0,7680.0,8000.0,7750.0,7400.0,7380.0,8400.0,8400.0,889.0,1125.0,864.0,1101.0,7428.0,7435.0,7360.0,7245.0,3540.77,1547.39,1453.26,7432.0,,,,,,,,7700.0,7630.0,7670.0,7700.0,7780.0,7800.0,7850.0,7720.0,7900.0,7953.0,8250.0,8100.0,7610.0,8050.0,7660.0,8050.0,8100.0,8000.0,7900.0,8050.0,8200.0,7780.0,,7673.0,7800.0,7800.0,8000.0,7840.0,7890.0,7850.0,7750.0,8250.0,8050.0,7600.0,7800.0,8050.0,8100.0,8600.0,8100.0,8600.0,8050.0,8250.0,8350.0,7850.0,8400.0,8200.0,8500.0,8250.0,8310.0,7350.0,7950.0,7520.0,7700.0,7570.0,7720.0,7650.0,7900.0,7900.0,,11200.0,9100.0,300.0,10300.0,-266.7646,,,,-465.0,,,-450.5844,1899.8035,-614.7839,-556.0709,,,,,,,,,,-68.0,,-681.0,575.0,,,107.6151,160.0,7.0,,,,,,,180.0,100.0,280.0,320.0,550.0,1000.0,900.0,946.0,,,,,-45.0,,415.0,-0.0074,,,,,,,,,,,,,,,,,35.9375,,67.0,,,,,,,,,,,,,,,,,,,,,929.655,78.5256,,,73.615,,,-1.33,73.38,,0.947,,2.547,61.43,69.65,75.3,68.73,30.14,97.52,83.33,,,1.1947,,0.9861,,,10.3816,,,575.5,,,,,,9.5138,,,,66.898,74.08,0.6133,0.0695,-0.0616,,,75.3697,9.6274,9.4346,22.42,8.0,8.21,5.13,3.78,0.7,4.81,8.05,3.24,0.0,4.32,0.14,0.4,1.4,1.4,2.66,-35.0,,,,,,,,,,,,11.17,,,8990.0,2572.9,3.86,106.673,2310.0,2472.0,6945.0,,618.61,7.2271,3739.76,5310.0,,,,,7353.333333333333
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2024-11-14,-369.669,,,,-1193.0,7290.0,8600.0,9624.5,10520.0,,-260.6829,,,,,,5750.0,541.88,465.0,,,,,,,,,,,,7475.0,7432.0,7290.0,7500.0,7680.0,8000.0,7750.0,7400.0,7380.0,8400.0,8400.0,889.0,1125.0,864.0,1101.0,7428.0,7435.0,7360.0,7245.0,3540.77,1547.39,1453.26,7432.0,,,,,,,,7700.0,7630.0,7670.0,7700.0,7780.0,7800.0,7850.0,7720.0,7900.0,7953.0,8250.0,8100.0,7610.0,8050.0,7660.0,8050.0,8100.0,8000.0,7900.0,8050.0,8200.0,7780.0,,7673.0,7800.0,7800.0,8000.0,7840.0,7890.0,7850.0,7750.0,8250.0,8050.0,7600.0,7800.0,8050.0,8100.0,8600.0,8100.0,8600.0,8050.0,8250.0,8350.0,7850.0,8400.0,8200.0,8500.0,8250.0,8310.0,7350.0,7950.0,7520.0,7700.0,7570.0,7720.0,7650.0,7900.0,7900.0,,11200.0,9100.0,300.0,10300.0,-267.2203,-274.2831,,,-465.0,-17.0,3767.5,-399.6606,1899.8035,-614.7839,-556.0709,-581.5579,-554.6069,628.525,2771.2618,2623.0715,2551.3279,2516.1736,,2056.8482,-68.0,70.0,-681.0,575.0,487.0,885.0,107.6151,160.0,7.0,9.0,-8.0,-1.0,-194.5442,-174.2831,-609.1914,180.0,100.0,280.0,320.0,550.0,1000.0,900.0,968.0,968.0,-17.0,1931.2,3027.766,-45.0,885.0,415.0,-0.0074,,,,,,,,,,,,,,,,,35.9375,,67.0,,,,,,,,,,,,,,,,,,,,,929.655,78.5256,,,73.615,,,-1.33,73.38,,0.947,,2.547,61.43,69.65,75.3,68.73,30.14,97.52,83.33,,,1.2551,,0.9861,,,10.3816,,,575.5,,,,,,9.5138,,,,66.898,74.08,0.6133,0.0695,-0.0616,,,75.3697,9.6274,9.4346,22.42,8.0,8.21,5.13,3.78,0.7,4.81,8.05,3.24,0.0,4.32,0.14,0.4,1.4,1.4,2.66,-35.0,,,,,,,,,,,,11.17,,,8990.0,2572.9,3.86,106.673,2310.0,2472.0,6945.0,5338.0,618.61,7.2271,3739.76,5310.0,8317.0,7432.0,7433.0,7441.0,7353.333333333333
|
||||||
2024-11-15,,,,,-1158.0,,,,,53.48,,28.24,56.08,15.69,2.75,0.5556,,,465.0,1208.91,8.49,6.84,7.82,46.8,48.45,39.63,65.25,23.68,785.79,5.58,7475.0,,,7500.0,7680.0,7980.0,7750.0,7400.0,7380.0,8400.0,8400.0,889.0,1125.0,864.0,1101.0,7467.0,7470.0,7360.0,7255.0,3505.44,1541.22,1447.37,7413.0,,10.64,10.45,1254.0,10.81,52.64,86.5,7700.0,7650.0,7720.0,7730.0,7810.0,7850.0,7850.0,7740.0,7880.0,7989.0,8250.0,8100.0,7640.0,8050.0,7690.0,8050.0,8100.0,8000.0,7900.0,8050.0,8250.0,7810.0,,7673.0,7800.0,7800.0,8000.0,7870.0,7920.0,7850.0,7750.0,8250.0,8050.0,7600.0,7800.0,8050.0,8100.0,8600.0,8100.0,8600.0,8050.0,8250.0,8350.0,7850.0,8400.0,8200.0,8500.0,8250.0,8310.0,7380.0,7900.0,7550.0,7700.0,7600.0,7720.0,7650.0,7900.0,7900.0,591.7,11200.0,9100.0,300.0,10300.0,-208.8558,,2637.8199,2537.8199,-450.0,,,-386.1469,1957.4645,-548.1224,-497.7261,,,,,,,,,,-107.0,,,585.0,,,199.9462,195.0,3.0,,,,,,,180.0,100.0,280.0,300.0,550.0,1000.0,900.0,946.0,,,,,-30.0,,430.0,-0.015,14.02,1.38,2.68,0.46,0.82,0.28,0.53,26.07,-0.2001,21.09,105.6128,3.93,0.0229,,,,33.5938,6.15,65.5,1810.0,11.71,11.9025,1806.9,38.03,13.39,11.96,8.12,51.42,5.47,57.25,5.76,1300.885,5.27,43.3,37628.0,2553.6,0.36,,,,,0.01,52.555,,-3.44,67.2169,,,6.21,,3.2388,,,,,,,,,66.5969,74.08,,1.1687,,-12.55,1.13,,-0.5281,,,,40.8186,16.626,50.0325,0.01,,65.2275,40.7343,0.2497,66.5969,,,,,-0.3011,0.0511,,,,23.43,8.0,7.98,4.55,4.6,1.4,4.81,8.98,3.24,0.23,4.32,0.14,0.4,0.7,0.7,1.96,-70.0,7.1575,8.31,3.96,11.295,2594.4,,,,0.79,282.0,5.63,10.81,0.36,,9002.5,2570.1,4.02,106.687,2310.0,,6930.0,,611.69,7.2294,3708.14,5282.0,,,,,7393.333333333333
|
2024-11-15,-338.235,,,,-1158.0,7300.0,8600.0,9630.5,10520.0,53.48,-251.6557,28.24,56.08,15.69,2.75,0.5556,5750.0,528.08,465.0,1208.91,8.49,6.84,7.82,46.8,48.45,39.63,65.25,23.68,785.79,5.58,7475.0,7413.0,7300.0,7500.0,7680.0,7980.0,7750.0,7400.0,7380.0,8400.0,8400.0,889.0,1125.0,864.0,1101.0,7467.0,7470.0,7360.0,7255.0,3505.44,1541.22,1447.37,7413.0,,10.64,10.45,1254.0,10.81,52.64,86.5,7700.0,7650.0,7720.0,7730.0,7810.0,7850.0,7850.0,7740.0,7880.0,7989.0,8250.0,8100.0,7640.0,8050.0,7690.0,8050.0,8100.0,8000.0,7900.0,8050.0,8250.0,7810.0,,7673.0,7800.0,7800.0,8000.0,7870.0,7920.0,7850.0,7750.0,8250.0,8050.0,7600.0,7800.0,8050.0,8100.0,8600.0,8100.0,8600.0,8050.0,8250.0,8350.0,7850.0,8400.0,8200.0,8500.0,8250.0,8310.0,7380.0,7900.0,7550.0,7700.0,7600.0,7720.0,7650.0,7900.0,7900.0,591.7,11200.0,9100.0,300.0,10300.0,-199.9154,-205.1937,2637.8199,2537.8199,-450.0,-57.0,3770.9,-281.413,1957.4645,-548.1224,-497.7261,-533.9345,-504.7596,628.5275,2789.3078,2650.7136,2591.0091,2559.1666,,2105.2435,-107.0,60.0,,585.0,483.0,876.0,199.9462,195.0,3.0,21.0,-17.0,-4.0,-185.6091,-105.1937,-529.1692,180.0,100.0,280.0,300.0,550.0,1000.0,900.0,987.0,987.0,-57.0,1978.2,3097.3946,-30.0,876.0,430.0,-0.015,14.02,1.38,2.68,0.46,0.82,0.28,0.53,26.07,-0.2001,21.09,105.6128,3.93,0.0229,,,,33.5938,6.15,65.5,1810.0,11.71,11.9025,1806.9,38.03,13.39,11.96,8.12,51.42,5.47,57.25,5.76,1300.885,5.27,43.3,37628.0,2553.6,0.36,,,929.655,80.0136,0.01,52.555,73.54,-3.44,67.2169,-1.96,73.7,6.21,0.944,3.2388,2.547,62.84,70.46,75.3,68.73,30.14,97.52,83.33,66.5969,74.08,1.2055,1.1687,0.9741,-12.55,1.13,10.4201,-0.5281,,569.5,,40.8186,16.626,50.0325,0.01,9.5247,65.2275,40.7343,0.2497,66.5969,74.0786,0.451,0.05,0.0415,-0.3011,0.0511,75.2097,9.6096,9.4761,23.43,8.0,7.98,4.55,4.6,1.4,4.81,8.98,3.24,0.23,4.32,0.14,0.4,0.7,0.7,1.96,-70.0,7.1575,8.31,3.96,11.295,2594.4,,,,0.79,282.0,5.63,10.81,0.36,,9002.5,2570.1,4.02,106.687,2310.0,,6930.0,5341.0,611.69,7.2294,3708.14,5282.0,8289.0,7413.0,7417.0,7434.0,7393.333333333333
|
||||||
2024-11-18,,,,,-1183.0,,,,,,,,,,,,,,,,,,,,,,,,,,7475.0,,,7490.0,7680.0,7980.0,7750.0,7400.0,7380.0,8400.0,8400.0,,,,,7428.0,7445.0,,7260.0,3537.41,1539.87,1447.26,7436.0,,,,,,,,7650.0,7650.0,7700.0,7730.0,7810.0,7850.0,7850.0,7780.0,7880.0,8006.0,8250.0,8100.0,7640.0,8050.0,7690.0,8100.0,8150.0,8000.0,7900.0,8050.0,8300.0,7810.0,,7603.0,7800.0,7800.0,8000.0,7870.0,7920.0,7850.0,7750.0,8250.0,8050.0,7600.0,7800.0,8050.0,8100.0,8600.0,8100.0,8600.0,8050.0,8250.0,8350.0,7850.0,8400.0,8200.0,8500.0,8250.0,8310.0,7370.0,7900.0,7550.0,7700.0,7600.0,7730.0,7650.0,7900.0,7900.0,,11200.0,9100.0,320.0,10300.0,,,2573.4739,2483.4739,-370.0,,,-353.6583,1914.0993,-514.5125,-540.7466,,,,,,,,,,,,,515.0,,,,170.0,17.0,,,,,,,190.0,90.0,280.0,300.0,550.0,1000.0,910.0,946.0,,,,,50.0,,,0.1111,,,,,,,,,,,,,,,,,42.2764,,70.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66.5969,,,,,,,,,,23.54,5.69,5.81,7.82,4.08,0.7,6.23,10.62,3.13,0.23,4.32,0.14,0.4,0.0,0.7,1.61,-45.0,,,,,,,,,,,,10.81,,,9072.5,2614.6,4.14,106.275,2340.0,,6850.0,,606.6,7.232,3727.05,5284.0,,,,,7363.333333333333
|
2024-11-18,-338.7818,,,,-1183.0,7280.0,8580.0,9623.5,10520.0,,-243.8643,,,,,,5750.0,546.48,,,,,,,,,,,,,7475.0,7436.0,7280.0,7490.0,7680.0,7980.0,7750.0,7400.0,7380.0,8400.0,8400.0,,,,,7428.0,7445.0,,7260.0,3537.41,1539.87,1447.26,7436.0,,,,,,,,7650.0,7650.0,7700.0,7730.0,7810.0,7850.0,7850.0,7780.0,7880.0,8006.0,8250.0,8100.0,7640.0,8050.0,7690.0,8100.0,8150.0,8000.0,7900.0,8050.0,8300.0,7810.0,,7603.0,7800.0,7800.0,8000.0,7870.0,7920.0,7850.0,7750.0,8250.0,8050.0,7600.0,7800.0,8050.0,8100.0,8600.0,8100.0,8600.0,8050.0,8250.0,8350.0,7850.0,8400.0,8200.0,8500.0,8250.0,8310.0,7370.0,7900.0,7550.0,7700.0,7600.0,7730.0,7650.0,7900.0,7900.0,,11200.0,9100.0,320.0,10300.0,,-69.8474,2573.4739,2483.4739,-370.0,-154.0,3805.8,-348.0652,1914.0993,-514.5125,-540.7466,-481.0919,-450.7957,628.655,2817.0848,2693.4718,2641.6341,2613.7215,,2162.8747,,,,515.0,586.0,817.0,,170.0,17.0,9.0,-7.0,-2.0,-165.0329,20.1526,-367.345,190.0,90.0,280.0,300.0,550.0,1000.0,910.0,964.0,964.0,-154.0,2057.6,3112.5762,50.0,817.0,,0.1111,,,,,,,,,,,,,,,,,42.2764,,70.0,,,,,,,,,,,,,,,,,,,,,872.715,80.8312,,,74.205,,,-2.04,73.88,,0.956,,2.391,62.61,66.91,75.48,72.55,30.82,97.61,82.22,,,1.2039,,0.9556,,,10.4549,,,576.5,,,,,,9.5231,,,,66.5969,74.0643,0.4428,0.0489,-0.0848,,,74.805,9.5657,9.4989,23.54,5.69,5.81,7.82,4.08,0.7,6.23,10.62,3.13,0.23,4.32,0.14,0.4,0.0,0.7,1.61,-45.0,,,,,,,,,,,,10.81,,,9072.5,2614.6,4.14,106.275,2340.0,,6850.0,5341.0,606.6,7.232,3727.05,5284.0,8253.0,7436.0,7438.0,7445.0,7363.333333333333
|
||||||
2024-11-19,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7470.0,7680.0,7980.0,7750.0,7400.0,7390.0,8400.0,8400.0,,,,,,,,,3583.65,1556.09,1455.05,,,,,,,,,7700.0,7670.0,7720.0,7730.0,7810.0,7850.0,7850.0,7790.0,7930.0,8020.0,8250.0,8100.0,7640.0,8050.0,7690.0,8100.0,8150.0,8000.0,7900.0,8050.0,8300.0,7810.0,,7623.0,7800.0,7800.0,8000.0,7870.0,7920.0,7850.0,7750.0,8150.0,8000.0,7600.0,7800.0,8050.0,8100.0,8600.0,8100.0,8600.0,8050.0,8250.0,8350.0,7850.0,8400.0,8200.0,8500.0,8250.0,8310.0,7370.0,7900.0,7550.0,7700.0,7600.0,7730.0,7650.0,7900.0,7900.0,,11200.0,9100.0,300.0,10300.0,,,2493.9802,2423.9802,,,,-423.7683,1835.0419,-587.0422,,,,,,,,,,,,,,,,,,,,,,,,,,210.0,70.0,280.0,300.0,550.0,1000.0,930.0,946.0,,,,,50.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66.5969,,,,,,,,,,22.59,5.51,5.34,7.58,5.95,0.0,6.0,11.99,3.13,0.0,4.78,0.14,0.4,0.0,0.7,2.08,,,,,,,,,,,,,10.81,,,,2634.7,4.12,106.206,,,,,615.08,7.2387,,,,,,,7370.0
|
2024-11-19,-310.3906,,,,,7280.0,8600.0,9602.5,10520.0,,-241.1214,,,,,,5750.0,558.44,,,,,,,,,,,,,,7490.0,7280.0,7470.0,7680.0,7980.0,7750.0,7400.0,7390.0,8400.0,8400.0,,,,,,,,,3583.65,1556.09,1455.05,,,,,,,,,7700.0,7670.0,7720.0,7730.0,7810.0,7850.0,7850.0,7790.0,7930.0,8020.0,8250.0,8100.0,7640.0,8050.0,7690.0,8100.0,8150.0,8000.0,7900.0,8050.0,8300.0,7810.0,,7623.0,7800.0,7800.0,8000.0,7870.0,7920.0,7850.0,7750.0,8150.0,8000.0,7600.0,7800.0,8050.0,8100.0,8600.0,8100.0,8600.0,8050.0,8250.0,8350.0,7850.0,8400.0,8200.0,8500.0,8250.0,8310.0,7370.0,7900.0,7550.0,7700.0,7600.0,7730.0,7650.0,7900.0,7900.0,,11200.0,9100.0,300.0,10300.0,,-140.4264,2503.949,2433.949,,-178.0,3859.8,-448.1357,1835.0419,-587.0422,,,,628.4725,2815.4191,2693.9594,2638.1342,2600.0933,,,,,,,640.0,851.0,,,,-11.0,4.0,7.0,,,-429.3882,210.0,70.0,280.0,300.0,550.0,1000.0,930.0,910.0,910.0,-178.0,2130.8,3166.5762,50.0,851.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,817.965,84.8471,,,74.315,,,-1.13,74.75,,0.951,,2.241,62.61,66.91,76.2,75.44,31.28,97.61,82.22,,,1.2005,,0.9507,,,10.5619,,,597.5,,,,,,9.5121,,,,66.5969,73.9786,0.56,0.0619,0.112,,,74.6877,9.5534,9.6109,22.59,5.51,5.34,7.58,5.95,0.0,6.0,11.99,3.13,0.0,4.78,0.14,0.4,0.0,0.7,2.08,,,,,,,,,,,,,10.81,,,9087.5,2631.0,3.92,106.206,,,,5307.0,615.08,7.2394,,,8341.0,7490.0,7483.0,7479.0,7370.0
|
||||||
|
|||||||
|
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@ -218,7 +218,7 @@ def ex_Model(df,horizon,input_size,train_steps,val_check_steps,early_stop_patien
|
|||||||
return nf_test_preds
|
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):
|
def model_losss(sqlitedb):
|
||||||
global dataset
|
global dataset
|
||||||
# 预测数据处理 predict
|
# 预测数据处理 predict
|
||||||
@ -483,12 +483,10 @@ def model_losss(sqlitedb):
|
|||||||
plt.close()
|
plt.close()
|
||||||
return model_results3
|
return model_results3
|
||||||
|
|
||||||
|
# 聚烯烃计算预测评估指数
|
||||||
|
|
||||||
|
|
||||||
# 计算预测评估指数
|
|
||||||
def model_losss_juxiting(sqlitedb):
|
def model_losss_juxiting(sqlitedb):
|
||||||
global dataset
|
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 = [sqlitedb.select_data('most_model',columns=['most_common_model'],order_by='ds desc',limit=1).values[0][0]]
|
||||||
most_model_name = most_model[0]
|
most_model_name = most_model[0]
|
||||||
|
|
||||||
@ -556,292 +554,6 @@ def model_losss_juxiting(sqlitedb):
|
|||||||
plt.close()
|
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
|
# # 历史数据+预测数据
|
||||||
# # 拼接未来时间预测
|
# # 拼接未来时间预测
|
||||||
df_predict = pd.read_csv(os.path.join(dataset,'predict.csv'))
|
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)
|
sqlitedb.insert_data('trueandpredict',tuple(row_dict.values()),columns=columns)
|
||||||
|
|
||||||
first_row_to_database(df_predict)
|
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():
|
def find_most_common_model():
|
||||||
# 最多频率的模型名称
|
# 最多频率的模型名称
|
||||||
min_model_max_frequency_model = df_combined3['min_model'].tail(20).value_counts().idxmax()
|
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]
|
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 = df_predict.copy()
|
||||||
df_predict2['ds'] = pd.to_datetime(df_predict2['ds'])
|
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_prediction'] = min_abs_error_rate_predictions
|
||||||
df_combined3['min_abs_error_rate_column_name'] = min_abs_error_rate_column_name
|
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
|
# 判断 df 的数值列转为float
|
||||||
for col in df_combined3.columns:
|
for col in df_combined3.columns:
|
||||||
try:
|
try:
|
||||||
@ -942,13 +683,13 @@ def model_losss_juxiting(sqlitedb):
|
|||||||
|
|
||||||
|
|
||||||
def _plt_predict_ture(df):
|
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
|
df = df[-lens:] # 取180个数据点画图
|
||||||
# 历史价格
|
# 历史价格
|
||||||
plt.figure(figsize=(20, 10))
|
plt.figure(figsize=(20, 10))
|
||||||
plt.plot(df['ds'], df['y'], label='真实值')
|
plt.plot(df['ds'], df['y'], label='真实值')
|
||||||
# 颜色填充
|
# 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')
|
# plt.plot(df_combined3['ds'], df_combined3['min_abs_error_rate_prediction'], label='最小绝对误差', linestyle='--', color='orange')
|
||||||
# 网格
|
# 网格
|
||||||
plt.grid(True)
|
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:
|
except TimeoutError as e:
|
||||||
print(f"请求超时: {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'):
|
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
|
global y
|
||||||
# 创建内容对应的空列表
|
# 创建内容对应的空列表
|
||||||
@ -1577,31 +1317,12 @@ def pp_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inputsi
|
|||||||
# print(f'绘制第{i+1}个特征{col}与价格散点图时出错：{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
|
### 添加标题
|
||||||
content.append(Graphs.draw_title(f'{y}{time}预测报告'))
|
content.append(Graphs.draw_title(f'{y}{time}预测报告'))
|
||||||
|
|
||||||
### 预测结果
|
### 预测结果
|
||||||
content.append(Graphs.draw_little_title('一、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
|
# 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')))
|
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
|
# 取df中y列为空的行
|
||||||
import pandas as pd
|
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。'))
|
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')
|
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_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：'))
|
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)
|
eval_df[col] = eval_df[col].round(3)
|
||||||
# 筛选 fivemodels_list.tolist() 的行
|
# 筛选 fivemodels_list.tolist() 的行
|
||||||
eval_df = eval_df[eval_df['模型(Model)'].isin(fivemodels_list)]
|
eval_df = eval_df[eval_df['模型(Model)'].isin(fivemodels_list)]
|
||||||
# df转置
|
|
||||||
eval_df = eval_df.T
|
eval_df = eval_df.T
|
||||||
# df重置索引
|
|
||||||
eval_df = eval_df.reset_index()
|
eval_df = eval_df.reset_index()
|
||||||
eval_df = eval_df.T
|
eval_df = eval_df.T
|
||||||
# # 添加表格
|
|
||||||
data = eval_df.values.tolist()
|
data = eval_df.values.tolist()
|
||||||
col_width = 500/len(eval_df.columns)
|
col_width = 500/len(eval_df.columns)
|
||||||
content.append(Graphs.draw_table(col_width,*data))
|
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('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('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：'))
|
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')))
|
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
|
# 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)):
|
for i in range(len(fuyi)):
|
||||||
content.append(Graphs.draw_text(f'{i+1}、{fuyi[i]}'))
|
content.append(Graphs.draw_text(f'{i+1}、{fuyi[i]}'))
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
### 生成pdf文件
|
### 生成pdf文件
|
||||||
doc = SimpleDocTemplate(os.path.join(dataset,reportname), pagesize=letter)
|
doc = SimpleDocTemplate(os.path.join(dataset,reportname), pagesize=letter)
|
||||||
# doc = SimpleDocTemplate(os.path.join(dataset,'reportname.pdf'), pagesize=letter)
|
# doc = SimpleDocTemplate(os.path.join(dataset,'reportname.pdf'), pagesize=letter)
|
||||||
doc.build(content)
|
doc.build(content)
|
||||||
# pdf 上传到数字化信息平台
|
# pdf 上传到数字化信息平台
|
||||||
# 读取pdf并转为base64
|
|
||||||
try:
|
try:
|
||||||
if is_update_report:
|
if is_update_report:
|
||||||
with open(os.path.join(dataset,reportname), 'rb') as f:
|
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:
|
except TimeoutError as e:
|
||||||
print(f"请求超时: {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'):
|
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
|
global y
|
||||||
# 创建内容对应的空列表
|
# 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:
|
except TimeoutError as e:
|
||||||
print(f"请求超时: {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'):
|
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
|
# 创建内容对应的空列表
|
||||||
content = list()
|
content = list()
|
||||||
|
|||||||
Binary file not shown.
Loading…
Reference in New Issue
Block a user