├── results ├── images │ ├── Doge Univariate Mape Fig1.png │ ├── Doge Univariate Mape Fig2.png │ ├── Doge Univariate Rmse Fig1.png │ ├── Doge Univariate Rmse Fig2.png │ ├── Eth Univariate Mape Fig1.png │ ├── Eth Univariate Mape Fig2.png │ ├── Eth Univariate Rmse Fig1.png │ ├── Eth Univariate Rmse Fig2.png │ ├── Lite Univariate Mape Fig1.png │ ├── Lite Univariate Mape Fig2.png │ ├── Lite Univariate Rmse Fig1.png │ ├── Lite Univariate Rmse Fig2.png │ ├── Bitcoin Univariate Mape Fig1.png │ ├── Bitcoin Univariate Mape Fig2.png │ ├── Bitcoin Univariate Rmse Fig1.png │ ├── Bitcoin Univariate Rmse Fig2.png │ ├── Doge Multivariate Mape Fig1.png │ ├── Doge Multivariate Mape Fig2.png │ ├── Doge Multivariate Rmse Fig1.png │ ├── Doge Multivariate Rmse Fig2.png │ ├── Eth Multivariate Mape Fig1.png │ ├── Eth Multivariate Mape Fig2.png │ ├── Eth Multivariate Rmse Fig1.png │ ├── Eth Multivariate Rmse Fig2.png │ ├── Lite Multivariate Mape Fig1.png │ ├── Lite Multivariate Mape Fig2.png │ ├── Lite Multivariate Rmse Fig1.png │ ├── Lite Multivariate Rmse Fig2.png │ ├── Bitcoin Multivariate Mape Fig1.png │ ├── Bitcoin Multivariate Mape Fig2.png │ ├── Bitcoin Multivariate Rmse Fig1.png │ └── Bitcoin Multivariate Rmse Fig2.png ├── model ranking.csv ├── confidence_interval │ ├── Lite_Univariate_Mape_Confidence_Interval.csv │ ├── Lite_Univariate_Rmse_Confidence_Interval.csv │ ├── Bitcoin_Multivariate_Mape_Confidence_Interval.csv │ ├── Doge_Univariate_Rmse_Confidence_Interval.csv │ ├── Eth_Univariate_Rmse_Confidence_Interval.csv │ ├── Bitcoin_Multivariate_Rmse_Confidence_Interval.csv │ ├── Doge_Multivariate_Rmse_Confidence_Interval.csv │ ├── Eth_Multivariate_Rmse_Confidence_Interval.csv │ ├── Lite_Multivariate_Rmse_Confidence_Interval.csv │ ├── Eth_Univariate_Mape_Confidence_Interval.csv │ ├── Lite_Multivariate_Mape_Confidence_Interval.csv │ ├── Eth_Multivariate_Mape_Confidence_Interval.csv │ ├── Doge_Multivariate_Mape_Confidence_Interval.csv │ ├── Doge_Univariate_Mape_Confidence_Interval.csv │ ├── Bitcoin_Univariate_Mape_Confidence_Interval.csv │ └── Bitcoin_Univariate_Rmse_Confidence_Interval.csv ├── Bitcoin │ ├── Bitcoin_Univariate_Mape.csv │ └── Bitcoin_Multivariate_Mape.csv ├── Eth │ └── Eth_Univariate_Mape.csv └── Lite │ └── Lite_Univariate_Mape.csv ├── draw ├── actual.py ├── Volatility.py └── experiment2.py ├── README.md ├── confidence_draw.py └── main.py /results/images/Doge Univariate Mape Fig1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sydney-machine-learning/deeplearning-crypto/HEAD/results/images/Doge Univariate Mape Fig1.png -------------------------------------------------------------------------------- /results/images/Doge Univariate Mape Fig2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sydney-machine-learning/deeplearning-crypto/HEAD/results/images/Doge Univariate 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,,,,,,,,,,,,,, 4 | btc_multi,rmse,3,1,2,5,6,4,mape,3,2,1,5,6,4 5 | ,,,,,,,,,,,,,, 6 | eth_uni,rmse,1,3,2,5,6,4,mape,1,5,2,4,3,6 7 | ,,,,,,,,,,,,,, 8 | eth_multi,rmse,5,4,3,1,2,6,mape,2,4,5,6,1,3 9 | ,,,,,,,,,,,,,, 10 | doge_uni,rmse,4,3,1,6,2,5,mape,4,2,5,3,1,6 11 | ,,,,,,,,,,,,,, 12 | doge_multi,rmse,2,3,1,6,4,5,mape,2,3,1,6,5,4 13 | ,,,,,,,,,,,,,, 14 | ltc_uni,rmse,4,3,2,5,1,6,mape,2,3,1,5,4,6 15 | ,,,,,,,,,,,,,, 16 | ltc_multi,rmse,1,3,5,6,2,4,mape,1,3,2,6,4,5 17 | ,,,,,,,,,,,,,, 18 | ,,3,2.875,2.125,4.875,3.125,5,,2,3.25,2.375,5,3.375,5 19 | -------------------------------------------------------------------------------- /draw/actual.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import matplotlib.pyplot as plt 3 | import numpy as np 4 | 5 | df = pd.read_csv(r'./BTC-USD2019.csv', parse_dates=['Date'], index_col='Date') 6 | # 设置字体大小 7 | plt.rcParams.update({'font.size': 8}) 8 | 9 | fig, ax = plt.subplots(figsize=(10, 6)) # 创建图形和坐标轴对象，并设置图形大小 10 | 11 | # 绘制折线图 12 | ax.plot(df.index, df['Close'], color='blue', linestyle='-') 13 | 14 | # 添加标题和标签 15 | ax.set_xlabel('Time', fontsize=12) # 设置x轴标签 16 | ax.set_ylabel('Close Price (USD)', fontsize=12) # 设置y轴标签 17 | 18 | # 获取选中时间段的索引范围 19 | start_date = '2020-03-01' 20 | end_date = '2024-04-04' 21 | 22 | # 选中时间段并将整个图形区域标红并降低透明度 23 | ticks = ax.get_yticks() 24 | tick_interval = ticks[1] - ticks[0] 25 | new_max_tick = max(ticks) + tick_interval*3 26 | 27 | # 计算新的刻度间隔（假设间隔为原始间隔） 28 | new_tick_interval = tick_interval 29 | 30 | # 设置新的 y 轴刻度范围和刻度间隔 31 | ax.set_ylim(ticks[0], new_max_tick) 32 | ax.set_yticks(np.arange(ticks[0], new_max_tick, new_tick_interval)) 33 | # 再次获取新的刻度值 34 | ticks = ax.get_yticks() 35 | 36 | # 重新设置 y 轴刻度范围 37 | ax.set_ylim(ticks[0], ticks[-1]) 38 | 39 | # 计算选中时间段的纵向距离 40 | highlight_height = ticks[-1] - ticks[0] 41 | 42 | # 添加选中时间段的红色区域 43 | highlight_color = 'red' 44 | highlight_alpha = 0.3 45 | ax.axvspan(start_date, end_date, ymin=0, ymax=(ticks[-2]/ticks[-1]), color=highlight_color, alpha=highlight_alpha) 46 | 47 | # 显示图形 48 | plt.tight_layout() # 自动调整布局，防止标签重叠 49 | plt.show() 50 | -------------------------------------------------------------------------------- /results/confidence_interval/Lite_Univariate_Mape_Confidence_Interval.csv: -------------------------------------------------------------------------------- 1 | ,LSTM Train MAPE,LSTM Test MAPE,ED-LSTM Train MAPE,ED-LSTM Test MAPE,BD-LSTM Train MAPE,BD-LSTM Test MAPE,CNN Train MAPE,CNN Test MAPE,Covn-LSTM Train MAPE,Covn-LSTM Test MAPE,Transformer Train MAPE,Transformer Test MAPE 2 | step-1,"[13.2252, 27.7683]","[4.4873, 5.2382]","[17.8934, 33.7753]","[4.794, 5.6616]","[17.5361, 28.8595]","[4.3336, 4.8307]","[12.8514, 17.7608]","[6.6286, 7.0489]","[10.4021, 15.5289]","[4.6162, 4.928]","[27.3117, 45.3129]","[6.5266, 7.5988]" 3 | step-2,"[12.5773, 24.2291]","[6.3768, 6.8539]","[18.6744, 33.766]","[6.4536, 7.2143]","[15.9857, 42.7932]","[5.8462, 7.3888]","[13.7116, 17.879]","[7.744, 8.1085]","[12.205, 17.462]","[6.5295, 6.8353]","[28.3019, 43.9252]","[7.9655, 9.3103]" 4 | step-3,"[15.1026, 27.6001]","[7.9163, 8.3977]","[19.2602, 34.4425]","[8.0281, 8.6039]","[18.0918, 48.5299]","[7.2381, 9.0033]","[14.0959, 17.9182]","[9.0296, 9.4249]","[12.3718, 18.7225]","[8.1732, 8.5756]","[26.7158, 40.979]","[9.5196, 10.2879]" 5 | step-4,"[15.4124, 29.58]","[9.2907, 9.8374]","[19.5776, 34.3009]","[9.2694, 9.861]","[17.622, 45.8956]","[8.3518, 9.9242]","[14.1414, 18.1764]","[10.3415, 10.8124]","[12.9714, 19.3273]","[9.7873, 10.368]","[24.0405, 38.6942]","[10.7779, 11.7177]" 6 | step-5,"[18.0124, 31.1156]","[10.6317, 11.0417]","[20.3319, 34.9498]","[10.3392, 10.9439]","[20.458, 52.0723]","[9.5781, 11.3116]","[16.0877, 20.8065]","[11.4781, 12.0542]","[15.6516, 20.0469]","[11.0837, 11.7253]","[29.1814, 39.3745]","[11.7233, 12.692]" 7 | -------------------------------------------------------------------------------- /results/confidence_interval/Lite_Univariate_Rmse_Confidence_Interval.csv: -------------------------------------------------------------------------------- 1 | ,LSTM Train MINMAX RMSE,LSTM Test MINMAX RMSE,ED-LSTM Train MINMAX RMSE,ED-LSTM Test MINMAX RMSE,BD-LSTM Train MINMAX RMSE,BD-LSTM Test MINMAX RMSE,CNN Train MINMAX RMSE,CNN Test MINMAX RMSE,Covn-LSTM Train MINMAX RMSE,Covn-LSTM Test MINMAX RMSE,Transformer Train MINMAX RMSE,Transformer Test MINMAX RMSE 2 | step-1,"[0.01, 0.0133]","[0.0362, 0.0401]","[0.0186, 0.0213]","[0.0342, 0.0397]","[0.0161, 0.0177]","[0.0298, 0.0337]","[0.0162, 0.0193]","[0.0382, 0.0398]","[0.0105, 0.0136]","[0.033, 0.0355]","[0.0261, 0.0302]","[0.0691, 0.0761]" 3 | step-2,"[0.0122, 0.0165]","[0.0455, 0.0503]","[0.0217, 0.0244]","[0.0462, 0.0512]","[0.0231, 0.0251]","[0.0382, 0.042]","[0.0165, 0.0184]","[0.0455, 0.0485]","[0.0134, 0.017]","[0.0451, 0.049]","[0.0238, 0.0293]","[0.0671, 0.0753]" 4 | step-3,"[0.014, 0.0182]","[0.0588, 0.065]","[0.0204, 0.0241]","[0.0595, 0.0666]","[0.0242, 0.0279]","[0.0479, 0.0536]","[0.0183, 0.0206]","[0.0594, 0.0661]","[0.0139, 0.0162]","[0.0583, 0.0648]","[0.0218, 0.026]","[0.07, 0.0792]" 5 | step-4,"[0.0156, 0.0201]","[0.073, 0.0806]","[0.0226, 0.0267]","[0.0698, 0.0814]","[0.0271, 0.0309]","[0.0549, 0.0627]","[0.0208, 0.0236]","[0.082, 0.1077]","[0.0152, 0.018]","[0.0778, 0.0856]","[0.0223, 0.025]","[0.0781, 0.0856]" 6 | step-5,"[0.0177, 0.0229]","[0.0824, 0.0899]","[0.0257, 0.0298]","[0.0771, 0.0904]","[0.0297, 0.0331]","[0.0665, 0.0733]","[0.0228, 0.0267]","[0.0961, 0.1302]","[0.0174, 0.0204]","[0.0876, 0.095]","[0.0263, 0.0286]","[0.0859, 0.0924]" 7 | -------------------------------------------------------------------------------- /results/confidence_interval/Bitcoin_Multivariate_Mape_Confidence_Interval.csv: -------------------------------------------------------------------------------- 1 | ,LSTM Train MAPE,LSTM Test MAPE,ED-LSTM Train MAPE,ED-LSTM Test MAPE,BD-LSTM Train MAPE,BD-LSTM Test MAPE,CNN Train MAPE,CNN Test MAPE,Covn-LSTM Single Train MAPE,Covn-LSTM Test MAPE,Transformer Train MAPE,Transformer Test MAPE 2 | step-1,"[10.278, 15.833]","[4.8016, 5.5793]","[10.1073, 15.3695]","[4.4431, 5.1697]","[11.8545, 19.9838]","[3.7574, 4.6475]","[11.7458, 15.7381]","[9.4997, 10.6952]","[7.0251, 9.6154]","[11.7574, 14.2351]","[14.2277, 24.742]","[8.9512, 10.0217]" 3 | step-2,"[10.2299, 14.9598]","[5.9332, 6.8602]","[10.9479, 16.3239]","[5.5227, 6.1601]","[12.2502, 20.8327]","[4.9086, 5.7555]","[12.3209, 15.9513]","[10.3717, 11.5193]","[6.8778, 9.1808]","[10.6307, 12.2771]","[12.7715, 27.4906]","[9.8155, 11.5321]" 4 | step-3,"[10.8624, 15.8207]","[7.0131, 8.0646]","[11.4892, 16.6527]","[6.3722, 7.0218]","[13.7595, 21.1281]","[6.013, 6.6243]","[11.3814, 14.8702]","[11.6071, 12.8574]","[7.648, 9.7496]","[13.3983, 15.394]","[13.3365, 21.536]","[10.5762, 11.6469]" 5 | step-4,"[9.3756, 15.4245]","[7.7479, 8.7253]","[12.1632, 17.6116]","[7.098, 7.7688]","[14.3777, 21.127]","[6.8823, 7.5224]","[12.0493, 15.8533]","[12.1667, 13.4957]","[7.832, 9.9269]","[15.4514, 18.3678]","[16.1949, 23.2823]","[11.0379, 11.9061]" 6 | step-5,"[9.9233, 13.8697]","[8.2672, 9.2328]","[12.6784, 17.9691]","[7.6803, 8.3669]","[16.809, 26.0111]","[7.7761, 8.4948]","[13.1646, 16.906]","[12.7586, 14.2702]","[8.219, 9.9487]","[18.3475, 23.0561]","[14.9598, 28.2582]","[11.7128, 12.9811]" 7 | -------------------------------------------------------------------------------- /results/confidence_interval/Doge_Univariate_Rmse_Confidence_Interval.csv: -------------------------------------------------------------------------------- 1 | ,LSTM Train MINMAX RMSE,LSTM Test MINMAX RMSE,ED-LSTM Train MINMAX RMSE,ED-LSTM Test MINMAX RMSE,BD-LSTM Train MINMAX RMSE,BD-LSTM Test MINMAX RMSE,CNN Train MINMAX RMSE,CNN Test MINMAX RMSE,Conv-LSTM Train MINMAX RMSE,Conv-LSTM Test MINMAX RMSE,Transformer Train MINMAX RMSE,Transformer Test MINMAX RMSE 2 | step-1,"[0.0205, 0.0229]","[0.137, 0.1614]","[0.0177, 0.0186]","[0.125, 0.1521]","[0.0178, 0.0225]","[0.0494, 0.0523]","[0.0334, 0.0361]","[0.1223, 0.2204]","[0.021, 0.0246]","[0.2023, 0.4849]","[0.115, 0.1374]","[0.1755, 0.2476]" 3 | step-2,"[0.0283, 0.0297]","[0.1389, 0.1657]","[0.0239, 0.0249]","[0.129, 0.1547]","[0.0237, 0.0276]","[0.0545, 0.0578]","[0.0394, 0.0431]","[0.179, 0.3451]","[0.0291, 0.0321]","[0.2042, 0.3889]","[0.1009, 0.1944]","[0.1712, 0.2745]" 4 | step-3,"[0.0338, 0.0351]","[0.1454, 0.1701]","[0.0288, 0.0296]","[0.1274, 0.1528]","[0.0296, 0.0333]","[0.0697, 0.0748]","[0.0422, 0.0455]","[0.173, 0.3842]","[0.0355, 0.0393]","[0.3391, 0.5299]","[0.1127, 0.1354]","[0.1834, 0.3036]" 5 | step-4,"[0.0371, 0.0382]","[0.151, 0.1764]","[0.0327, 0.0334]","[0.1301, 0.1544]","[0.032, 0.0351]","[0.0627, 0.0669]","[0.0444, 0.0479]","[0.3906, 0.5818]","[0.042, 0.0459]","[0.4394, 0.6291]","[0.1181, 0.1377]","[0.1716, 0.2722]" 6 | step-5,"[0.0409, 0.0422]","[0.1548, 0.1814]","[0.0357, 0.0364]","[0.131, 0.1546]","[0.0347, 0.0378]","[0.0625, 0.0665]","[0.0451, 0.0481]","[0.4203, 0.6139]","[0.0453, 0.0497]","[0.5963, 0.808]","[0.1149, 0.136]","[0.1727, 0.2649]" 7 | -------------------------------------------------------------------------------- /results/confidence_interval/Eth_Univariate_Rmse_Confidence_Interval.csv: -------------------------------------------------------------------------------- 1 | ,LSTM Train MINMAX RMSE,LSTM Test MINMAX RMSE,ED-LSTM Train MINMAX RMSE,ED-LSTM Test MINMAX RMSE,BD-LSTM Train MINMAX RMSE,BD-LSTM Test MINMAX RMSE,CNN Train MINMAX RMSE,CNN Test MINMAX RMSE,Conv-LSTM Train MINMAX RMSE,Conv-LSTM Test MINMAX RMSE,Transformer Train MINMAX RMSE,Transformer Test MINMAX RMSE 2 | step-1,"[0.0123, 0.0155]","[0.0266, 0.0293]","[0.0144, 0.0187]","[0.0249, 0.0272]","[0.0161, 0.0178]","[0.0253, 0.0278]","[0.0279, 0.0302]","[0.0372, 0.0396]","[0.0108, 0.0141]","[0.0307, 0.0347]","[0.0195, 0.0282]","[0.0302, 0.0373]" 3 | step-2,"[0.0167, 0.0213]","[0.0329, 0.0354]","[0.0187, 0.0242]","[0.0354, 0.0393]","[0.0245, 0.0268]","[0.0333, 0.037]","[0.0271, 0.0306]","[0.0437, 0.047]","[0.0132, 0.0181]","[0.0389, 0.0431]","[0.024, 0.0348]","[0.0369, 0.0454]" 4 | step-3,"[0.0196, 0.0238]","[0.0361, 0.0383]","[0.0224, 0.0282]","[0.0388, 0.0423]","[0.0271, 0.0304]","[0.0381, 0.042]","[0.0274, 0.0313]","[0.0451, 0.0504]","[0.0157, 0.0209]","[0.0467, 0.0527]","[0.0244, 0.0343]","[0.041, 0.0488]" 5 | step-4,"[0.0225, 0.028]","[0.0425, 0.0458]","[0.0259, 0.0321]","[0.0442, 0.0469]","[0.0305, 0.0345]","[0.0402, 0.0445]","[0.0288, 0.033]","[0.0491, 0.0551]","[0.0182, 0.0238]","[0.0561, 0.0656]","[0.0259, 0.0361]","[0.049, 0.0553]" 6 | step-5,"[0.0254, 0.0319]","[0.0453, 0.0487]","[0.0288, 0.0351]","[0.0459, 0.0483]","[0.0358, 0.0402]","[0.0473, 0.0522]","[0.0334, 0.038]","[0.0551, 0.0621]","[0.021, 0.027]","[0.0688, 0.0814]","[0.0284, 0.0389]","[0.0499, 0.0571]" 7 | -------------------------------------------------------------------------------- /results/confidence_interval/Bitcoin_Multivariate_Rmse_Confidence_Interval.csv: -------------------------------------------------------------------------------- 1 | ,LSTM Train MINMAX RMSE,LSTM Test MINMAX RMSE,ED LSTM Train MINMAX RMSE,ED LSTM Test MINMAX RMSE,BD LSTM Train MINMAX RMSE,BD LSTM Test MINMAX RMSE,CNN Train MINMAX RMSE,CNN Test MINMAX RMSE,Covn-LSTM Train MINMAX RMSE,Covn-LSTM Test MINMAX RMSE,Transformer Train MINMAX RMSE,Transformer Test MINMAX RMSE 2 | step-1,"[0.0139, 0.018]","[0.0291, 0.0328]","[0.0154, 0.0193]","[0.0269, 0.0312]","[0.0156, 0.0168]","[0.0229, 0.0264]","[0.0198, 0.0233]","[0.0405, 0.0427]","[0.0107, 0.0138]","[0.0463, 0.0538]","[0.014, 0.0214]","[0.0356, 0.0408]" 3 | step-2,"[0.0176, 0.0232]","[0.0367, 0.0424]","[0.0195, 0.0242]","[0.0339, 0.0373]","[0.0223, 0.0233]","[0.0314, 0.0357]","[0.0225, 0.0267]","[0.0461, 0.0493]","[0.0127, 0.0178]","[0.0447, 0.0485]","[0.0158, 0.0237]","[0.0394, 0.0441]" 4 | step-3,"[0.0205, 0.0265]","[0.0413, 0.0485]","[0.0218, 0.0271]","[0.0369, 0.0398]","[0.0243, 0.0265]","[0.039, 0.0441]","[0.0211, 0.0251]","[0.0516, 0.0552]","[0.0156, 0.021]","[0.0584, 0.0773]","[0.0159, 0.0242]","[0.0438, 0.048]" 5 | step-4,"[0.0225, 0.0283]","[0.0427, 0.0489]","[0.0235, 0.0287]","[0.0391, 0.0418]","[0.0264, 0.0296]","[0.0436, 0.0488]","[0.0203, 0.0229]","[0.0555, 0.0594]","[0.016, 0.0213]","[0.0742, 0.1031]","[0.0162, 0.0243]","[0.0481, 0.0522]" 6 | step-5,"[0.023, 0.0289]","[0.0446, 0.0489]","[0.0254, 0.0306]","[0.0416, 0.0444]","[0.0279, 0.0321]","[0.0486, 0.0537]","[0.019, 0.0214]","[0.0586, 0.0627]","[0.0165, 0.0223]","[0.1061, 0.1593]","[0.0167, 0.025]","[0.0503, 0.0548]" 7 | -------------------------------------------------------------------------------- /results/confidence_interval/Doge_Multivariate_Rmse_Confidence_Interval.csv: -------------------------------------------------------------------------------- 1 | ,LSTM Train MINMAX RMSE,LSTM Test MINMAX RMSE,ED-LSTM Train MINMAX RMSE,ED-LSTM Test MINMAX RMSE,BD-LSTM Train MINMAX RMSE,BD-LSTM Test MINMAX RMSE,CNN Train MINMAX RMSE,CNN Test MINMAX RMSE,Conv-LSTM Train MINMAX RMSE,Conv-LSTM Test MINMAX RMSE,Transformer Train MINMAX RMSE,Transformer Test MINMAX RMSE 2 | step-1,"[0.0247, 0.0268]","[0.1619, 0.1793]","[0.0246, 0.0281]","[0.1618, 0.2039]","[0.0218, 0.0245]","[0.0552, 0.0681]","[0.0472, 0.0534]","[0.7909, 0.8334]","[0.0212, 0.0253]","[0.051, 0.1482]","[0.0291, 0.0469]","[0.2272, 0.2671]" 3 | step-2,"[0.0321, 0.0332]","[0.1671, 0.182]","[0.0303, 0.0327]","[0.1618, 0.2027]","[0.0257, 0.0279]","[0.0553, 0.0654]","[0.0532, 0.0573]","[0.5572, 0.7156]","[0.0289, 0.034]","[0.7873, 0.9161]","[0.0348, 0.0535]","[0.2265, 0.2699]" 4 | step-3,"[0.0379, 0.0392]","[0.1746, 0.1911]","[0.0338, 0.0362]","[0.1615, 0.2016]","[0.0297, 0.0322]","[0.0581, 0.0658]","[0.0578, 0.0633]","[0.6026, 0.7424]","[0.0343, 0.0438]","[0.0958, 0.2607]","[0.0425, 0.0626]","[0.229, 0.2712]" 5 | step-4,"[0.0409, 0.0422]","[0.1727, 0.1893]","[0.037, 0.0392]","[0.1626, 0.2017]","[0.0326, 0.0348]","[0.0621, 0.0685]","[0.0635, 0.0689]","[0.5815, 0.7496]","[0.0398, 0.0502]","[0.0719, 0.1745]","[0.0427, 0.0635]","[0.213, 0.2559]" 6 | step-5,"[0.0444, 0.0458]","[0.173, 0.1919]","[0.0401, 0.0421]","[0.1631, 0.2015]","[0.0358, 0.0382]","[0.0678, 0.0762]","[0.0686, 0.0744]","[0.5101, 0.6842]","[0.0441, 0.0555]","[0.105, 0.2688]","[0.0421, 0.0633]","[0.2129, 0.2535]" 7 | -------------------------------------------------------------------------------- /results/confidence_interval/Eth_Multivariate_Rmse_Confidence_Interval.csv: -------------------------------------------------------------------------------- 1 | ,LSTM Train MINMAX RMSE,LSTM Test MINMAX RMSE,ED-LSTM Train MINMAX RMSE,ED-LSTM Test MINMAX RMSE,BD-LSTM Train MINMAX RMSE,BD-LSTM Test MINMAX RMSE,CNN Train MINMAX RMSE,CNN Test MINMAX RMSE,Conv-LSTM Train MINMAX RMSE,Conv-LSTM Test MINMAX RMSE,Transformer Train MINMAX RMSE,Transformer Test MINMAX RMSE 2 | step-1,"[0.0171, 0.0212]","[0.1757, 0.2068]","[0.0194, 0.0228]","[0.1454, 0.2177]","[0.0188, 0.0197]","[0.0875, 0.1192]","[0.0161, 0.0182]","[0.0969, 0.1045]","[0.0128, 0.0155]","[0.2323, 0.4424]","[0.0115, 0.0167]","[0.2323, 0.2605]" 3 | step-2,"[0.0203, 0.0249]","[0.1678, 0.2058]","[0.0228, 0.0273]","[0.1532, 0.2379]","[0.0247, 0.0254]","[0.1435, 0.2142]","[0.0196, 0.0218]","[0.1124, 0.1646]","[0.0143, 0.0185]","[0.1726, 0.2755]","[0.012, 0.0176]","[0.2349, 0.2621]" 4 | step-3,"[0.0252, 0.0309]","[0.1816, 0.2171]","[0.027, 0.0325]","[0.1421, 0.2303]","[0.029, 0.0307]","[0.1485, 0.2112]","[0.0236, 0.0262]","[0.112, 0.2008]","[0.0163, 0.0215]","[0.4839, 0.6584]","[0.0123, 0.0185]","[0.244, 0.274]" 5 | step-4,"[0.0293, 0.0356]","[0.1707, 0.2068]","[0.0302, 0.0366]","[0.1422, 0.2339]","[0.0318, 0.0336]","[0.1456, 0.2025]","[0.0241, 0.0277]","[0.1113, 0.1182]","[0.0168, 0.0219]","[0.5818, 0.7315]","[0.0129, 0.0197]","[0.2361, 0.2654]" 6 | step-5,"[0.0338, 0.0402]","[0.1543, 0.1992]","[0.0338, 0.0404]","[0.1437, 0.2389]","[0.0349, 0.037]","[0.1658, 0.2263]","[0.0271, 0.0317]","[0.1253, 0.1299]","[0.0188, 0.0249]","[0.6715, 0.8068]","[0.0143, 0.0226]","[0.2316, 0.2623]" 7 | -------------------------------------------------------------------------------- /results/confidence_interval/Lite_Multivariate_Rmse_Confidence_Interval.csv: -------------------------------------------------------------------------------- 1 | ,LSTM Train MINMAX RMSE,LSTM Test MINMAX RMSE,ED LSTM Train MINMAX RMSE,ED LSTM Test MINMAX RMSE,BD LSTM Train MINMAX RMSE,BD LSTM Test MINMAX RMSE,CNN Train MINMAX RMSE,CNN Test MINMAX RMSE,Covn-LSTM Train MINMAX RMSE,Covn-LSTM Test MINMAX RMSE,Transformer Train MINMAX RMSE,Transformer Test MINMAX RMSE 2 | step-1,"[0.0094, 0.0134]","[0.0573, 0.0676]","[0.0128, 0.0168]","[0.081, 0.107]","[0.0138, 0.0164]","[0.0748, 0.097]","[0.0152, 0.0175]","[0.418, 0.5116]","[0.0079, 0.0101]","[0.0664, 0.0859]","[0.0096, 0.0143]","[0.1401, 0.1662]" 3 | step-2,"[0.0111, 0.0158]","[0.0718, 0.0857]","[0.0136, 0.0183]","[0.0996, 0.1246]","[0.0166, 0.0203]","[0.1252, 0.1788]","[0.0133, 0.0158]","[0.4109, 0.4996]","[0.0087, 0.0115]","[0.1177, 0.1768]","[0.0101, 0.0141]","[0.1516, 0.1732]" 4 | step-3,"[0.0121, 0.0177]","[0.0863, 0.0978]","[0.014, 0.0184]","[0.0916, 0.1149]","[0.0183, 0.022]","[0.2072, 0.2953]","[0.0144, 0.0168]","[0.4365, 0.5255]","[0.0096, 0.0124]","[0.1059, 0.1502]","[0.0103, 0.0144]","[0.164, 0.1807]" 5 | step-4,"[0.0134, 0.0188]","[0.0842, 0.0929]","[0.0146, 0.0194]","[0.0796, 0.0869]","[0.0205, 0.0236]","[0.2315, 0.3362]","[0.0157, 0.0185]","[0.4626, 0.5546]","[0.0102, 0.0133]","[0.1135, 0.1349]","[0.0113, 0.0164]","[0.1542, 0.18]" 6 | step-5,"[0.0146, 0.0201]","[0.0875, 0.0963]","[0.0162, 0.0216]","[0.0844, 0.0917]","[0.0233, 0.0265]","[0.2259, 0.3261]","[0.0173, 0.0207]","[0.4675, 0.566]","[0.0115, 0.0153]","[0.1431, 0.2076]","[0.0114, 0.0167]","[0.1522, 0.1778]" 7 | -------------------------------------------------------------------------------- /results/confidence_interval/Eth_Univariate_Mape_Confidence_Interval.csv: -------------------------------------------------------------------------------- 1 | ,LSTM Train MAPE,LSTM Test MAPE,ED-LSTM Train MAPE,ED-LSTM Test MAPE,BD-LSTM Train MAPE,BD-LSTM Test MAPE,CNN Train MAPE,CNN Test MAPE,Conv-LSTM Train MAPE,Conv-LSTM Test MAPE,Transformer Train MAPE,Transformer Test MAPE 2 | step-1,"[34.018, 73.7928]","[4.7359, 6.754]","[59.4909, 118.0335]","[6.0891, 9.1841]","[44.2873, 99.8586]","[5.0159, 8.0773]","[52.5912, 74.1698]","[6.7743, 8.0204]","[23.8257, 36.1019]","[5.603, 6.1491]","[85.7631, 119.345]","[12.0157, 15.3822]" 3 | step-2,"[39.4515, 74.2738]","[6.2665, 8.0437]","[64.8373, 126.7742]","[7.4185, 10.4867]","[57.9771, 117.4073]","[6.5457, 9.9568]","[53.0131, 82.9733]","[8.0203, 9.2941]","[16.7953, 25.6309]","[7.3745, 7.9036]","[72.7794, 115.0949]","[13.0931, 16.3615]" 4 | step-3,"[35.6667, 70.7154]","[7.1994, 8.9646]","[66.8134, 127.3784]","[8.5469, 11.3823]","[65.2639, 114.6017]","[7.8924, 10.411]","[62.2032, 97.4528]","[8.9245, 10.0332]","[19.6762, 25.1944]","[8.4538, 9.094]","[77.0224, 113.7273]","[13.3951, 15.9749]" 5 | step-4,"[39.4385, 70.6251]","[8.1519, 9.555]","[64.5447, 124.7953]","[9.2971, 11.9589]","[59.9505, 100.6872]","[8.6097, 10.1516]","[62.5268, 97.604]","[10.0322, 11.2412]","[21.3332, 29.5412]","[9.7882, 10.5974]","[80.5854, 107.9963]","[13.8493, 16.8094]" 6 | step-5,"[41.8461, 72.5423]","[9.1909, 10.5222]","[65.9942, 126.152]","[10.1663, 12.7352]","[64.3044, 101.1619]","[9.6193, 10.9515]","[60.5436, 93.6412]","[10.8435, 11.9796]","[25.3446, 37.5532]","[11.3525, 12.4604]","[77.9395, 126.8938]","[14.3487, 17.871]" 7 | -------------------------------------------------------------------------------- /results/confidence_interval/Lite_Multivariate_Mape_Confidence_Interval.csv: -------------------------------------------------------------------------------- 1 | ,LSTM Train MAPE,LSTM Test MAPE,ED-LSTM Train MAPE,ED-LSTM Test MAPE,BD-LSTM Train MAPE,BD-LSTM Test MAPE,CNN Train MAPE,CNN Test MAPE,Conv-LSTM Train MAPE,Conv-LSTM Test MAPE,Transformer Train MAPE,Transformer Test MAPE 2 | step-1,"[16.2358, 30.0958]","[11.0453, 18.3415]","[10.9568, 18.1643]","[17.0339, 24.562]","[16.444, 29.0128]","[11.7569, 14.7616]","[12.4456, 19.6094]","[57.5597, 83.7381]","[10.9614, 16.1999]","[28.2191, 37.4584]","[18.0624, 38.5935]","[35.9053, 42.3749]" 3 | step-2,"[14.557, 26.4325]","[12.7042, 20.254]","[12.4392, 19.8443]","[18.3523, 25.6317]","[14.5816, 28.9174]","[14.7721, 18.886]","[13.6298, 20.5017]","[61.1708, 87.2052]","[12.3809, 17.5326]","[67.5042, 96.5711]","[17.0828, 31.157]","[38.7299, 45.1997]" 4 | step-3,"[15.1047, 27.5177]","[13.9886, 20.9816]","[13.1758, 20.0884]","[18.8306, 26.034]","[19.4601, 35.1632]","[18.8798, 23.5365]","[14.3337, 19.9472]","[78.1534, 113.194]","[11.9925, 17.8767]","[32.0964, 52.299]","[18.28, 23.0865]","[43.9375, 49.7327]" 5 | step-4,"[15.6594, 26.2494]","[15.0496, 22.0879]","[13.7104, 20.5762]","[19.4283, 26.4425]","[19.6877, 33.433]","[21.8434, 32.0391]","[14.2556, 19.2461]","[98.2782, 146.5235]","[13.7961, 19.3367]","[37.9544, 51.5417]","[18.3409, 25.4977]","[45.9791, 51.7855]" 6 | step-5,"[17.293, 28.5917]","[15.6829, 22.5097]","[14.3665, 21.0669]","[19.9513, 26.8163]","[23.7479, 33.6312]","[24.2728, 40.0305]","[15.9983, 22.9196]","[107.2007, 156.8459]","[13.678, 19.3821]","[46.5053, 69.0927]","[16.9861, 30.2788]","[46.4281, 52.4348]" 7 | -------------------------------------------------------------------------------- /results/confidence_interval/Eth_Multivariate_Mape_Confidence_Interval.csv: -------------------------------------------------------------------------------- 1 | ,LSTM Train MAPE,LSTM Test MAPE,ED-LSTM Train MAPE,ED-LSTM Test MAPE,BD-LSTM Train MAPE,BD-LSTM Test MAPE,CNN Train MAPE,CNN Test MAPE,Conv-LSTM Train MAPE,Conv-LSTM Test MAPE,Transformer Train MAPE,Transformer Test MAPE 2 | step-1,"[71.1495, 125.3609]","[13.7237, 17.759]","[87.354, 177.5595]","[23.5033, 42.9113]","[78.5877, 148.5062]","[12.1796, 23.7639]","[48.4968, 70.5129]","[43.1735, 48.3647]","[46.96, 77.2922]","[60.9274, 86.617]","[69.9909, 144.7148]","[26.5008, 29.1959]" 3 | step-2,"[73.5721, 137.3216]","[16.9075, 21.7916]","[87.4993, 176.2802]","[26.9328, 46.0661]","[77.1272, 135.9908]","[21.1178, 41.6849]","[53.4303, 78.5348]","[54.9595, 62.9072]","[45.2569, 68.5102]","[47.6378, 60.8835]","[75.4457, 158.719]","[28.1715, 30.9641]" 4 | step-3,"[77.3352, 150.5576]","[18.7994, 24.0048]","[87.3779, 178.0386]","[27.9435, 46.6445]","[103.8689, 171.8007]","[26.353, 47.8778]","[64.5482, 95.0648]","[36.92, 43.9397]","[43.6237, 67.6503]","[63.8801, 97.9861]","[57.403, 231.4084]","[28.2933, 30.7629]" 5 | step-4,"[90.0539, 168.0963]","[19.2434, 24.0964]","[85.9906, 177.4809]","[28.799, 47.1363]","[114.115, 197.0249]","[31.1495, 60.9349]","[80.6851, 118.4512]","[27.3546, 33.562]","[45.4848, 93.414]","[91.4803, 154.6812]","[62.3175, 284.136]","[29.2086, 31.8409]" 6 | step-5,"[92.0393, 167.0496]","[19.9392, 23.7325]","[86.9353, 178.533]","[29.4506, 47.5555]","[118.7917, 205.1261]","[37.5707, 79.5717]","[83.4045, 133.2062]","[28.2517, 34.3914]","[45.9016, 85.1696]","[109.1657, 183.372]","[74.9242, 199.9918]","[29.2451, 31.5631]" 7 | -------------------------------------------------------------------------------- /results/confidence_interval/Doge_Multivariate_Mape_Confidence_Interval.csv: -------------------------------------------------------------------------------- 1 | ,LSTM Train MAPE,LSTM Test MAPE,ED-LSTM Train MAPE,ED-LSTM Test MAPE,BD-LSTM Train MAPE,BD-LSTM Test MAPE,CNN Train MAPE,CNN Test MAPE,Covn-LSTM Single Train MAPE,Covn-LSTM Test MAPE,Transformer Train MAPE,Transformer Test MAPE 2 | step-1,"[57.1348, 99.6284]","[15.1754, 18.5917]","[102.1068, 217.2275]","[18.8387, 28.9399]","[85.8646, 168.4389]","[14.8044, 21.3862]","[47.6138, 112.4541]","[250.4312, 302.2255]","[28.0424, 49.1752]","[159.6337, 249.4565]","[194.0347, 548.4058]","[62.1587, 94.3693]" 3 | step-2,"[69.4131, 141.7375]","[17.6979, 22.6364]","[97.6328, 243.0917]","[19.8217, 31.3657]","[74.6865, 136.156]","[12.8345, 18.203]","[50.2769, 109.18]","[360.2467, 445.2433]","[31.8869, 54.0421]","[179.3931, 309.5036]","[189.1148, 460.3465]","[64.0337, 85.9803]" 4 | step-3,"[60.5803, 111.7619]","[18.1209, 22.2434]","[100.0807, 233.6913]","[20.1297, 31.0244]","[105.4671, 202.6972]","[16.5506, 25.187]","[54.5645, 113.6293]","[471.1085, 559.7075]","[33.4486, 62.5211]","[117.0119, 170.2058]","[122.5499, 548.1783]","[62.1058, 100.9486]" 5 | step-4,"[62.5611, 138.447]","[19.2094, 25.1477]","[100.7784, 238.8682]","[20.7509, 31.7546]","[97.4945, 191.4296]","[15.5316, 22.9462]","[54.1435, 120.9027]","[601.9424, 729.5422]","[32.5252, 63.5324]","[170.1383, 261.3518]","[165.0763, 671.6946]","[63.3078, 109.6453]" 6 | step-5,"[53.5167, 105.7995]","[19.3634, 22.7371]","[101.0452, 237.9335]","[21.2034, 32.0737]","[93.4918, 198.3097]","[17.5019, 26.042]","[58.5001, 109.3722]","[694.8821, 847.4328]","[39.0932, 73.3885]","[233.5707, 359.2679]","[121.1663, 234.1998]","[54.6477, 68.6676]" 7 | -------------------------------------------------------------------------------- /results/confidence_interval/Doge_Univariate_Mape_Confidence_Interval.csv: -------------------------------------------------------------------------------- 1 | ,LSTM Train MINMAX RMSE,LSTM Test MINMAX RMSE,ED-LSTM Train MINMAX RMSE,ED-LSTM Test MINMAX RMSE,BD-LSTM Train MINMAX RMSE,BD-LSTM Test MINMAX RMSE,CNN Train MINMAX RMSE,CNN Test MINMAX RMSE,Conv-LSTM Train MINMAX RMSE,Conv-LSTM Test MINMAX RMSE,Transformer Train MINMAX RMSE,Transformer Test MINMAX RMSE 2 | step-1,"[104.0647, 211.0476]","[18.9866, 29.2839]","[84.1507, 260.2552]","[15.7199, 30.0434]","[135.9151, 329.5]","[15.2401, 32.9864]","[35.0298, 94.3313]","[16.2446, 22.1451]","[37.2939, 78.1556]","[18.945, 28.7007]","[284.3438, 400.0931]","[60.8248, 80.6615]" 3 | step-2,"[97.0872, 197.7535]","[19.4875, 28.8881]","[77.4164, 252.6325]","[15.4054, 32.7049]","[108.474, 238.656]","[13.9914, 25.3373]","[32.3377, 83.0166]","[18.5528, 24.4348]","[34.8265, 87.7834]","[19.0293, 24.2452]","[347.3526, 569.4939]","[67.1969, 91.1906]" 4 | step-3,"[82.8808, 181.5684]","[18.587, 28.4779]","[87.3385, 253.8771]","[16.4374, 32.069]","[95.4429, 471.0852]","[14.4761, 48.2766]","[38.5352, 91.8154]","[20.2452, 26.46]","[45.9528, 81.8778]","[22.3275, 27.0237]","[331.4292, 621.9211]","[64.5188, 91.2642]" 5 | step-4,"[83.9952, 197.2391]","[20.5467, 34.7237]","[85.578, 255.4261]","[16.8365, 32.8818]","[103.7546, 618.5924]","[15.2917, 61.6747]","[47.6348, 100.6356]","[26.7674, 33.3215]","[44.8815, 92.7072]","[26.4779, 34.7195]","[334.7289, 504.2663]","[59.7641, 84.8567]" 6 | step-5,"[72.7513, 215.2461]","[20.6952, 35.5612]","[88.0259, 255.8059]","[17.3898, 33.1098]","[155.8726, 604.599]","[20.4687, 60.2541]","[45.2144, 90.8708]","[27.5823, 33.7549]","[48.3681, 89.0562]","[34.7063, 40.8082]","[323.6628, 454.3492]","[57.2863, 75.0522]" 7 | -------------------------------------------------------------------------------- /draw/Volatility.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import matplotlib.pyplot as plt 3 | 4 | # 读取数据 5 | df = pd.read_csv(r'./DOGE 2015.9-2024.4 月度数据.csv', parse_dates=['Date'], index_col='Date') 6 | 7 | # 设置字体大小 8 | plt.rcParams.update({'font.size': 10}) 9 | 10 | # 将数据重新采样为每月第一个工作日的数据 11 | monthly_open = df['Open'].resample('MS').first() 12 | 13 | # 计算每个月相对于前一个月的收益率 14 | df['Monthly_Return'] = monthly_open.pct_change() 15 | 16 | # 移除可能存在的NaN值 17 | df.dropna(subset=['Monthly_Return'], inplace=True) 18 | 19 | # 计算平均月收益率 20 | mean_monthly_return = df['Monthly_Return'].mean() 21 | 22 | # 计算每月收益率的方差 23 | df['Monthly_Variance'] = (df['Monthly_Return'] - mean_monthly_return) ** 2 24 | 25 | # 根据年份和月份分组计算波动率 26 | monthly_volatility = df.groupby([df.index.year, df.index.month])['Monthly_Variance'].mean() ** 0.5 27 | 28 | # 将年份和月份转换为数值型数据 29 | monthly_volatility.index = monthly_volatility.index.map(lambda x: str(x[0]) + '-' + str(x[1]).zfill(2)) 30 | 31 | # 提取每年的1月和7月 32 | yearly_months = [month for month in monthly_volatility.index if month.endswith('-01') or month.endswith('-07')] 33 | 34 | 35 | fig,ax=plt.subplots() 36 | # 分别绘制每年的月波动率 37 | ax.bar(range(len(monthly_volatility)), monthly_volatility.values, color='blue') 38 | 39 | # 设置横坐标刻度和标签 40 | ax.set_xticks(monthly_volatility.index.get_indexer(yearly_months), yearly_months,rotation=45) # 每年的1月和7月标签 41 | 42 | 43 | # 添加标题和标签 44 | ax.set_xlabel('Month', fontsize=14) 45 | ax.set_ylabel('Monthly Volatility', fontsize=14) 46 | 47 | 48 | start_date = '2020-03' 49 | end_date = '2024-04' 50 | 51 | # 获取选中时间段的索引范围 52 | start_index = monthly_volatility.index.get_loc(start_date) 53 | end_index = monthly_volatility.index.get_loc(end_date) 54 | 55 | # 选中时间段并将整个图形区域标红并降低透明度 56 | highlight_color = 'red' 57 | highlight_alpha = 0.3 58 | 59 | # 获取纵坐标的最大值 60 | yticks=ax.get_yticks() 61 | print("yticks:\n",yticks) 62 | ax.set_ylim(yticks[0],yticks[-1]) 63 | 64 | plt.axvspan(start_index - 0.5, end_index + 0.5, ymin=0, ymax=(yticks[-2]/yticks[-1]), color=highlight_color, alpha=highlight_alpha) 65 | 66 | 67 | # 显示图形 68 | plt.tight_layout() # 自动调整布局，防止标签重叠 69 | plt.show() 70 | -------------------------------------------------------------------------------- /results/confidence_interval/Bitcoin_Univariate_Mape_Confidence_Interval.csv: -------------------------------------------------------------------------------- 1 | ,LSTM Train MAPE,LSTM Test MAPE,ED-LSTM Train MAPE,ED-LSTM Test MAPE,BD-LSTM Train MAPE,BD-LSTM Test MAPE,CNN Train MAPE,CNN Test MAPE,Covn-LSTM Single Train MAPE,Covn-LSTM Test MAPE,Transformer Train MAPE,Transformer Test MAPE,MLP Train MAPE,MLP Test MAPE,ARIMA Train MAPE,ARIMA Test MAPE 2 | step-1,"[13.238695, 25.516548]","[3.617236, 4.981981]","[12.602078, 25.636945]","[3.714728, 5.101282]","[11.413508, 24.85817]","[3.24761, 4.883067]","[10.897179, 15.153265]","[6.141971, 7.028447]","[8.49815, 11.732406]","[3.693344, 4.286357]","[22.699817, 37.242919]","[6.492651, 8.764805]","[19.838318, 30.021863]","[5.362938, 6.550718]","[3.90353, 3.90353]","[7.592411, 7.592411]" 3 | step-2,"[11.538384, 23.488889]","[4.506525, 5.755391]","[14.103565, 29.493064]","[4.846932, 6.430778]","[13.341153, 30.186537]","[4.400546, 6.36294]","[10.841076, 15.258112]","[6.807055, 7.766142]","[8.841202, 11.696862]","[4.847408, 5.398492]","[19.299015, 42.545332]","[7.027698, 10.251164]","[17.992766, 28.956238]","[6.779454, 8.296227]","[3.90353, 3.90353]","[8.250092, 8.250092]" 4 | step-3,"[12.904222, 25.937465]","[5.521125, 6.804163]","[14.272919, 28.896836]","[5.641714, 7.073812]","[14.733641, 27.766907]","[5.391986, 6.731055]","[11.088062, 15.65481]","[7.820548, 8.685423]","[9.628519, 11.750498]","[5.801157, 6.349789]","[23.104969, 43.231837]","[8.122454, 10.895576]","[18.880653, 27.005667]","[8.147447, 10.163469]","[3.90353, 3.90353]","[8.747638, 8.747638]" 5 | step-4,"[12.683348, 22.337364]","[6.270159, 7.051742]","[14.973145, 29.791986]","[6.343776, 7.703336]","[14.21106, 26.545765]","[6.028924, 7.191251]","[12.87235, 18.719047]","[8.548033, 9.362527]","[10.258181, 12.436758]","[6.556714, 7.116402]","[22.801863, 38.142083]","[8.691541, 10.818471]","[18.743519, 27.476688]","[9.277789, 11.90568]","[3.90353, 3.90353]","[9.254003, 9.254003]" 6 | step-5,"[13.509384, 21.677597]","[6.966606, 7.562766]","[15.348047, 29.983716]","[6.986345, 8.285912]","[16.457144, 28.775504]","[6.903203, 7.935909]","[11.841223, 16.214499]","[9.310236, 10.179307]","[10.26319, 12.964111]","[7.371506, 7.856795]","[21.994481, 36.444927]","[9.239508, 10.861968]","[17.315319, 27.231781]","[10.551215, 13.225789]","[3.90353, 3.90353]","[9.842423, 9.842423]" 7 | -------------------------------------------------------------------------------- /results/confidence_interval/Bitcoin_Univariate_Rmse_Confidence_Interval.csv: -------------------------------------------------------------------------------- 1 | ,LSTM Train MINMAX RMSE,LSTM Test MINMAX RMSE,ED-LSTM Train MINMAX RMSE,ED-LSTM Test MINMAX RMSE,BD-LSTM Train MINMAX RMSE,BD-LSTM Test MINMAX RMSE,CNN Train MINMAX RMSE,CNN Test MINMAX RMSE,Covn-LSTM Train MINMAX RMSE,Covn-LSTM Test MINMAX RMSE,Transformer Train MINMAX RMSE,Transformer Test MINMAX RMSE,MLP Train MINMAX RMSE,MLP Test MINMAX RMSE,ARIMA Train MINMAX RMSE,ARIMA Test MINMAX RMSE 2 | step-1,"[0.013184, 0.015664]","[0.021057, 0.023604]","[0.015739, 0.017172]","[0.023596, 0.026325]","[0.013368, 0.014584]","[0.018731, 0.020379]","[0.02697, 0.029204]","[0.036516, 0.039578]","[0.012827, 0.015426]","[0.023158, 0.025612]","[0.017399, 0.02573]","[0.031958, 0.040108]","[0.0136, 0.014774]","[0.023383, 0.028236]","[0.012288, 0.012288]","[0.045789, 0.045789]" 3 | step-2,"[0.020679, 0.024878]","[0.031645, 0.035679]","[0.025098, 0.027897]","[0.033417, 0.03914]","[0.020552, 0.021996]","[0.025644, 0.028186]","[0.031934, 0.034486]","[0.04345, 0.046779]","[0.018383, 0.021254]","[0.029133, 0.031306]","[0.024333, 0.032074]","[0.039293, 0.046819]","[0.019848, 0.021056]","[0.031239, 0.040289]","[0.012288, 0.012288]","[0.049304, 0.049304]" 4 | step-3,"[0.027107, 0.030764]","[0.040048, 0.045046]","[0.0304, 0.033382]","[0.039082, 0.045064]","[0.025861, 0.027486]","[0.031899, 0.034751]","[0.034858, 0.037178]","[0.046261, 0.048858]","[0.023224, 0.02705]","[0.036626, 0.039655]","[0.030087, 0.03805]","[0.046387, 0.053643]","[0.024396, 0.026282]","[0.040117, 0.063202]","[0.012288, 0.012288]","[0.05214, 0.05214]" 5 | step-4,"[0.031064, 0.035365]","[0.04701, 0.052163]","[0.032749, 0.03562]","[0.042061, 0.047593]","[0.030585, 0.032155]","[0.037298, 0.039769]","[0.031816, 0.034949]","[0.052184, 0.055279]","[0.026479, 0.030243]","[0.041448, 0.045416]","[0.034727, 0.042392]","[0.052563, 0.060029]","[0.027484, 0.029937]","[0.047275, 0.087435]","[0.012288, 0.012288]","[0.054984, 0.054984]" 6 | step-5,"[0.031355, 0.034778]","[0.049391, 0.053694]","[0.034722, 0.037374]","[0.045206, 0.050215]","[0.032685, 0.03445]","[0.040849, 0.043908]","[0.031129, 0.03509]","[0.059486, 0.063691]","[0.028491, 0.033048]","[0.044535, 0.049083]","[0.038465, 0.04622]","[0.057726, 0.065584]","[0.030138, 0.031436]","[0.052305, 0.108967]","[0.012288, 0.012288]","[0.058066, 0.058066]" 7 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Time series model evaluation 2 | 3 | ## Train and Test 4 | 5 | ### Get Dataset 6 | 7 | we can use the `read_data` function to get the raw data, and then use `data_transform` function to normalize and de normalize the raw data 8 | 9 | **Example：** 10 | 11 | ```python 12 | # read raw data 13 | data, data_len = read_data(file_path, gold_path, dim_type, use_percentage) 14 | 15 | # split the dataset into train set and test set 16 | train_set, test_set = data[train_index], data[test_index] 17 | 18 | # normalize the dataset 19 | train_set, scalerstrain =data_trasform(train_set) 20 | ``` 21 | 22 | 23 | 24 | 25 | 26 | ### Create Model 27 | 28 | we can use the `create_model` to get the already defined model。 29 | 30 | `create_model(model_name, n_features, n_steps_in, n_steps_out)` 31 | 32 | * `model_name`：the already defined model name , such as LSTM, BD-LSTM, etc. 33 | * `n_features`：the model will used the number of the dataset features. Features include maximum price, minimum price, opening price, closing price, trading volume, and added gold price. 34 | * `n_steps_in`，`n_steps_out`：the model in and the model out size. 35 | 36 | **Example:** 37 | 38 | ```python 39 | model_name='LSTM' 40 | n_featues='Multi' 41 | n_steps_in = 5 42 | n_steps_out = 2 43 | 44 | myModel = create_model(model_name, n_features, n_steps_in, n_steps_out) 45 | ``` 46 | 47 | 48 | 49 | ### Train and Test 50 | 51 | we use the `train_and_forecast` function to train the model and predict the result. 52 | 53 | **Example：** 54 | 55 | ```python 56 | train_result, test_result = train_and_forecast(myModel, n_features, dim_type, train_set, test_set, n_steps_in,n_steps_out, epochs) 57 | ``` 58 | 59 | 60 | 61 | 62 | 63 | ### Evaluation 64 | 65 | We calculate RMSE and MAPE indicators based on the model prediction results to analyze the performance of the model. 66 | 67 | 68 | 69 | we use the `eval_result(result, n_steps_out, groud_truth, mode)` function to calc the RMSE and MAPE. 70 | 71 | * `result`：the model prediction results. 72 | * `n_steps_out`：the model output size 73 | * `ground_truth`：the ground truth 74 | * `mode`：choose the evaluation method, RMSE or MAPE. 75 | 76 | 77 | 78 | **Example:** 79 | 80 | * We can choose to calculate evaluation metrics for normalized data 81 | 82 | ```python 83 | train_minmax_rmse = eval_result(train_result, n_steps_out, train, 0) 84 | test_minmax_rmse = eval_result(test_result, n_steps_out, test, 0) 85 | ``` 86 | 87 | 88 | 89 | * Similarly, we can also choose to normalize the predicted results before calculating the relevant indicators. 90 | 91 | ## Results summary 92 | In this part, we will integrate the result data including the training and testing results during the 30 rounds experiments. 93 | We will calculate the RMSE and MAPE index from the above results data, and then plots Corresponding figures. 94 | 95 | 96 | The results data formate for input is excel.File Content main include RMSE or MAPE index about all models during training and testing. 97 | The summary and draw corresponding figures functioins are integrated into a `draw.py` python module,so we can import this module to gets the results quickly. 98 | 99 | 100 | Example: 101 | ```python 102 | from draw import Draw 103 | file_path=r'Bitcoin_Multivariate_Mape.xlsx' 104 | draw=Draw(file_path) 105 | draw.start() 106 | ``` 107 | 108 | ### Results Summary 109 | We provide the `Draw.start()` api to finish all the operations and get all results.Meanwhile, we also provide some 110 | extra apis for you to calculate intermediate results for analyse step by step. 111 | 112 | 113 | We provide `Draw.get_confidence_interval()` function for you to get the confidence interval from the origin input. 114 | 115 | 116 | Example: 117 | ```python 118 | from draw import Draw 119 | file_path=r'Bitcoin_Multivariate_Mape.xlsx' 120 | draw=Draw(file_path) 121 | draw.get_confidence_interval() 122 | ``` 123 | 124 | the calculated results will be saved in `csv` format by default. 125 | 126 | 127 | 128 | ### Draw 129 | In this part, we will visualize the confidence interval results by plotting corresponding figures. 130 | 131 | 132 | We provide an independent API(`draw1()` and `draw2()`)for you to draw corresponding images. 133 | 134 | ```python 135 | from draw import Draw 136 | file_path=r'Bitcoin_Multivariate_Mape.xlsx' 137 | draw=Draw(file_path) 138 | draw.draw1() 139 | 140 | draw.draw2() 141 | 142 | ``` 143 | In the `draw.py` module, we can set the bar_colors parameter to custom display color range. 144 | 145 | ### Select experiment 146 | We put the codes for experiments 1 and 2 into different python files. Experiments 1 and 2 used different datasets respectively. 147 | If you want to do experiment 1, please choose `main.py`, for experiment 2, please choose `Main2.py`. 148 | 149 | ### Publication 150 | 151 | * Wu, J., Zhang, X., Huang, F., Zhou, H., & Chandra, R. (2024). Review of deep learning models for crypto price prediction: implementation and evaluation. arXiv preprint arXiv:2405.11431: https://arxiv.org/abs/2405.11431 152 | 153 | #### Seminar 154 | * t~ai Seminar Series, [Review of deep learning models for crypto price prediction](https://youtu.be/KlkK6wnTqXQ) 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | -------------------------------------------------------------------------------- /confidence_draw.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | from ast import literal_eval 4 | from scipy import stats 5 | import matplotlib.pyplot as plt 6 | from pathlib import Path 7 | 8 | 9 | import matplotlib.pyplot as plt 10 | plt.rcParams['font.family'] = 'Times New Roman' 11 | plt.rcParams['font.size'] = 12 12 | bar_colors = ['#845EC2', '#4B4453', '#B0A8B9', '#C34A36', '#FF8066', '#4E8397','#926C00','#009EFA'] # 颜色 13 | 14 | class Draw: 15 | __file_path=None 16 | __save_name=None 17 | __save_name1=None 18 | __fig1_name=None 19 | __fig2_name=None 20 | __raw_data=None 21 | __column=None 22 | __steps=None 23 | __conf_data=None 24 | confidence = 0.95 # 置信区间 25 | 26 | def __init__(self, file_path): 27 | self.__file_path = Path(file_path) 28 | print("file path", self.__file_path) 29 | assert self.__check_path(), f'路径错误，或文件不存在' 30 | self.__get_names() 31 | self.__read_data() 32 | 33 | 34 | 35 | def __check_path(self): 36 | # 判断文件是否正常 37 | if not self.__file_path.exists(): 38 | print("文件不存在，路径错误1") 39 | return False 40 | else: 41 | return True 42 | 43 | def __get_names(self): 44 | self.__save_name = r'./results/'+self.__file_path.stem+"_Confidence_Interval.csv" 45 | self.__save_name1 = r'./results/' + self.__file_path.stem + "_Confidence_Interval_Mean_Error.csv" 46 | self.__fig1_name= r'./results/'+' '.join(self.__file_path.stem.split("_"))+" Fig1.png" 47 | self.__fig2_name= r'./results/'+' '.join(self.__file_path.stem.split("_"))+" Fig2.png" 48 | 49 | 50 | def __read_data(self): 51 | self.__raw_data=pd.read_excel(self.__file_path,index_col=0,header=0) 52 | 53 | # 读取excel后格式发生变化 54 | for column in self.__raw_data.columns.values: 55 | self.__raw_data[column] = self.__raw_data[column].apply(lambda x: np.array(literal_eval(x))) 56 | self.__columns = self.__raw_data.columns.values.tolist() 57 | self.__steps = self.__raw_data.values[0,0].size 58 | 59 | def __get_confidence_interval(self): 60 | ''' 61 | 计算置信区间并保存 62 | 保存实行（lower,upper） 63 | :return: 64 | ''' 65 | print("计算置信区间") 66 | index = [] 67 | for step in range(self.__steps): 68 | index.append("step-" + str(step + 1)) 69 | self.__conf_data = pd.DataFrame({}, columns=self.__columns, index=index) 70 | for column in self.__columns: 71 | model=self.__raw_data[column] 72 | # 将当前列转为二维矩阵形式 73 | matrix=np.empty([len(model.values), len(model.values[0])]) 74 | for row in range(len(model.values)): 75 | matrix[row]=model.values[row] 76 | cells=[] 77 | for step in range(self.__steps): 78 | mean=np.mean(matrix[:,step]) 79 | std=np.std(matrix[:,step]) 80 | # 计算置信区间 81 | z=stats.norm.ppf((1+self.confidence)/2) 82 | margin_of_error= z*(std/np.sqrt(len(model.values))) 83 | lower=round(mean-margin_of_error, 6) 84 | upper=round(mean+margin_of_error, 6) 85 | cells.append([lower, upper]) 86 | self.__conf_data[column]=cells 87 | # 保存置信区间结果 88 | self.__conf_data.to_csv(self.__save_name) 89 | 90 | def get_confidence_interval_mean_error(self): 91 | ''' 92 | 计算置信区间并保存 93 | 返回形式（mean,error） 94 | :return: 95 | ''' 96 | print("计算置信区间(mean,error)") 97 | index = [] 98 | for step in range(self.__steps): 99 | index.append("step-" + str(step + 1)) 100 | conf_data = pd.DataFrame({}, columns=self.__columns, index=index) 101 | for column in self.__columns: 102 | model=self.__raw_data[column] 103 | # 将当前列转为二维矩阵形式 104 | matrix=np.empty([len(model.values), len(model.values[0])]) 105 | for row in range(len(model.values)): 106 | matrix[row]=model.values[row] 107 | cells=[] 108 | for step in range(self.__steps): 109 | mean=np.mean(matrix[:,step]) 110 | std=np.std(matrix[:,step]) 111 | # 计算置信区间 112 | z=stats.norm.ppf((1+self.confidence)/2) 113 | margin_of_error= z*(std/np.sqrt(len(model.values))) 114 | cells.append([round(mean,6), round(margin_of_error,6)]) 115 | conf_data[column]=cells 116 | # 保存置信区间结果 117 | conf_data.to_csv(self.__save_name1) 118 | 119 | def __get_model_name(self, column): 120 | # 从列名中提取模型名 121 | if (" Train" in column): 122 | return column.split(" Train")[0] 123 | elif (" Test" in column): 124 | return column.split(" Test")[0] 125 | else: 126 | print("无法获取模型名，检查列名是否正确") 127 | 128 | def __draw1(self): 129 | ''' 130 | 绘制fig1 131 | :return: 132 | ''' 133 | print("绘制Fig1") 134 | boundary={} 135 | # 计算左右边界 136 | for idx, column in enumerate(self.__columns): 137 | model = self.__conf_data[column] 138 | matrix = np.empty([len(model.values), len(model.values[0])]) 139 | for row in range(len(model.values)): 140 | matrix[row] = model.values[row] 141 | # 计算数据 142 | boundary[column] = np.mean(matrix, axis=0) 143 | 144 | labels_train = [] 145 | labels_test = [] 146 | means_train = [] 147 | means_test = [] 148 | for key, value in boundary.items(): 149 | if "Train" in key: 150 | labels_train.append(key) 151 | means_train.append(round(np.mean(value), 6)) 152 | elif "Test" in key: 153 | labels_test.append(key) 154 | means_test.append(round(np.mean(value), 6)) 155 | model_num=len(labels_train) 156 | labels = labels_train + labels_test 157 | means = means_train + means_test 158 | 159 | yerr = [round(boundary[label][1] - means[idx], 6) for idx, label in enumerate(labels)] 160 | x = [i + 0.5 if i < model_num else i + 3.5 for i in range(len(means))] 161 | 162 | groups = 2 163 | fig, ax = plt.subplots(figsize=(8, 6)) 164 | ax.bar(x, means, width=1, color=bar_colors[0:int(len(labels) / groups)], label=labels) 165 | ax.errorbar(x, means, yerr, fmt='o', linewidth=1.5, capsize=3, ecolor="black") 166 | ax.set_xticks([np.mean([x[0 + model_num * i], x[model_num - 1 + model_num * i]]) for i in range(groups)], 167 | labels=["Train", "Test"]) 168 | ax.set_ylabel(self.__file_path.stem.split('_')[-1].upper()) 169 | labels = [self.__get_model_name(label) for label in labels] 170 | ax.legend(labels[0:int(len(labels) / groups)], loc='upper left') 171 | fig.savefig(self.__fig1_name, dpi=600, bbox_inches='tight') 172 | print("Fig1 Finished") 173 | 174 | def __draw2(self): 175 | # 绘制fig2 176 | print("绘制Fig2") 177 | 178 | # 进获取测试集数据 179 | columns = [column for column in self.__columns if "Test" in column] 180 | models_num=len(columns) 181 | x_ticks = ['step-'+str((step+1)) for step in range(self.__steps)] 182 | 183 | # 用于绘制每个step图形 184 | # 从筛选数据中获取labels,means,yerr数据 185 | means = [] 186 | errors = [] 187 | labels = columns * self.__steps 188 | 189 | for idx, label in enumerate(labels): 190 | step = idx//models_num 191 | cell=self.__conf_data[label].iloc[step] 192 | mean=round(np.mean(cell),6) 193 | error=round((cell[1]-mean),6) 194 | means.append(mean) 195 | errors.append(error) 196 | 197 | # 绘图 198 | x = [i + 0.5 + (i // models_num) * 3 for i in range(len(labels))] 199 | 200 | fig, ax = plt.subplots(figsize=(10, 8)) 201 | ax.bar(x, means, width=1, color=bar_colors[0:models_num], label=labels) 202 | ax.errorbar(x, means, errors, fmt='o', linewidth=1.5, capsize=3, ecolor="black") 203 | groups = int(len(x) / models_num) 204 | x_tixks_loc = [np.mean([x[0 + models_num * i], x[models_num - 1 + models_num * i]]) for i in range(groups)] 205 | ax.set_xticks(x_tixks_loc, labels=x_ticks) 206 | ax.set_ylabel(self.__file_path.stem.split('_')[-1].upper()) 207 | labels = [self.__get_model_name(label) for label in labels] 208 | ax.legend(labels[0:int(len(labels) / 5)], loc='upper left') 209 | # ax.set_title(name) 210 | fig.savefig(self.__fig2_name, dpi=600, bbox_inches='tight') 211 | print("Fig2 finished") 212 | 213 | def start(self): 214 | self.__get_confidence_interval() 215 | self.__draw1() 216 | self.__draw2() 217 | 218 | 219 | if __name__ == '__main__': 220 | file_path=r'./Bitcoin_Multivariate_Mape.xlsx' 221 | draw=Draw(file_path) 222 | draw.start() 223 | draw.get_confidence_interval_mean_error() 224 | 225 | 226 | 227 | 228 | -------------------------------------------------------------------------------- /draw/experiment2.py: -------------------------------------------------------------------------------- 1 | import keras 2 | import numpy as np 3 | import pandas as pd 4 | import tensorflow as tf 5 | import matplotlib 6 | import matplotlib.pyplot as plt 7 | from pathlib import Path 8 | from scipy import stats 9 | 10 | from sklearn.preprocessing import MinMaxScaler 11 | from tensorflow.keras.models import Sequential, Model 12 | from tensorflow.keras.layers import LSTM, Bidirectional, Dense, Conv1D, Flatten, MaxPooling1D, RepeatVector, \ 13 | TimeDistributed, LayerNormalization, Dropout, MultiHeadAttention, Input 14 | from tensorflow.keras.optimizers import Adam 15 | 16 | matplotlib.rcParams['font.family'] = 'Times New Roman' 17 | pd.set_option('mode.chained_assignment', None) 18 | 19 | 20 | def read_data(path, features): 21 | data = pd.read_csv(path) 22 | data["Date"] = pd.to_datetime(data["Date"], format='%Y-%m-%d').dt.date 23 | data.set_index('Date', inplace=True) 24 | data.sort_index(ascending=True, inplace=True) # 时间升序排列，旧日期在前面 25 | data = data[features] 26 | return data 27 | 28 | 29 | def get_model(model_name, n_steps_in, n_steps_out): 30 | model = Sequential() 31 | adam_optimizer = Adam(learning_rate=0.001) 32 | if model_name == 'BD LSTM': 33 | # mymodel.add(keras.Input(shape=(n_steps_in,50))) 34 | model.add(Bidirectional(LSTM(50, activation='sigmoid'), input_shape=(n_steps_in, 1))) 35 | model.add(Dense(n_steps_out)) 36 | elif model_name == 'ED LSTM': 37 | # Encoder-Decoder LSTM 38 | # Encoder 39 | print("here") 40 | model.add(LSTM(100, activation='sigmoid', input_shape=(n_steps_in, 1))) 41 | # Connector 42 | model.add(RepeatVector(n_steps_out)) 43 | # Decoder 44 | model.add(LSTM(100, activation='sigmoid', return_sequences=True)) 45 | model.add(TimeDistributed(Dense(1))) 46 | else: 47 | raise Exception("未找到模型") 48 | model.compile(optimizer=adam_optimizer, 49 | loss='mse', 50 | metrics=[ 51 | keras.metrics.RootMeanSquaredError(), 52 | keras.metrics.MeanAbsolutePercentageError() 53 | ]) 54 | return model 55 | 56 | 57 | def data_trasform(data, anti=False, scaler=None): 58 | # data为numpy数据 59 | if not anti: 60 | scaler = MinMaxScaler() 61 | data_norm = scaler.fit_transform(data) 62 | return data_norm, scaler 63 | else: 64 | return scaler.inverse_transform(data) 65 | 66 | 67 | def draw(data, save_name, step, confidence): 68 | # 选择与第一步骤相关的列 69 | pred_columns = [col for col in data.columns if col.endswith(f'_step{step}')] 70 | data_pred = data[pred_columns] 71 | 72 | # n 73 | n = data_pred.shape[1] 74 | # mean 75 | pred_mean = data_pred.mean(axis=1) 76 | # std 77 | pred_std = data_pred.std(axis=1) 78 | 79 | # 计算high、low 80 | t_critical = stats.t.ppf(1 - (1 - confidence) / 2, df=n - 1) 81 | high = pred_mean + (t_critical * pred_std / np.sqrt(n)) 82 | low = pred_mean - (t_critical * pred_std / np.sqrt(n)) 83 | 84 | data['Prediction_step' + str(step)] = pred_mean 85 | data['High_step' + str(step)] = high 86 | data['Low_step' + str(step)] = low 87 | 88 | # 绘图 89 | fig, ax = plt.subplots(figsize=(8, 6)) 90 | ax.plot(data.index, data['Actual'], color='orange') 91 | ax.plot(data.index, data['Prediction_step' + str(step)], color='blue') 92 | ax.fill_between(data.index, data['Low_step' + str(step)], data['High_step' + str(step)], color='gray', alpha=0.5) 93 | ax.legend(['Actual', f'Prediction Step {step}', 'Uncertainty']) 94 | ax.tick_params(axis='x', labelrotation=45) 95 | ax.tick_params(axis='both', labelsize=10) 96 | ax.set_xlabel("Date", fontsize=14) 97 | ax.set_ylabel("Close Price (USD)", fontsize=14) 98 | plt.tight_layout() 99 | plt.savefig(save_name, dpi=600) 100 | plt.show() 101 | return data 102 | 103 | 104 | # 计算每个步骤的置信区间 105 | def calculate_stepwise_confidence_intervals(df, n_steps_out, confidence=0.95): 106 | intervals_df = pd.DataFrame() 107 | for step in range(1, n_steps_out + 1): 108 | for metric in ['Train_RMSE', 'Test_RMSE', 'Train_MAPE', 'Test_MAPE']: 109 | values = df[f'{metric}_Step{step}'] 110 | mean = np.mean(values) 111 | std = np.std(values, ddof=1) 112 | n = len(values) 113 | t_critical = stats.t.ppf(1 - (1 - confidence) / 2, df=n - 1) 114 | margin_of_error = t_critical * std / np.sqrt(n) 115 | intervals_df.loc[f'{metric}_Step{step}', 'Lower_Bound'] = mean - margin_of_error 116 | intervals_df.loc[f'{metric}_Step{step}', 'Upper_Bound'] = mean + margin_of_error 117 | return intervals_df 118 | 119 | 120 | def eval_result(result, n_steps_out, target, mode): 121 | ''' 122 | evaluate the modl resule 123 | :param result:the model result 124 | :param n_steps_out:the days you predict 125 | :param target:the ground-true 126 | :param mode:the type of evaluation(you can choose 0：rmse,1：mape) 127 | :return:the evaluation result 128 | ''' 129 | if mode == 0: 130 | # return rmse result 131 | # 归一化 132 | result, _ = data_trasform(result) 133 | target, _ = data_trasform(target) 134 | # 下面需要修改 135 | rmse = [] 136 | for i in range(n_steps_out): 137 | rmse.append(np.sqrt(np.mean((result[:, i] - target[:, i]) ** 2))) 138 | return rmse 139 | 140 | elif mode == 1: 141 | # return MAPE result 142 | result = result + 0.0000001 143 | target = target + 0.0000001 144 | mape = [] 145 | for i in range(n_steps_out): 146 | mape.append(np.mean(np.abs((target[:, i] - result[:, i]) / target[:, i])) * 100) 147 | return mape 148 | else: 149 | return None 150 | 151 | 152 | def main(): 153 | print("start") 154 | # 初始化参数 155 | n_steps_in = 3 156 | n_features = 1 157 | n_steps_out = 1 158 | batch_size = 32 159 | epochs = 100 # epoch大小 160 | shuffle = True # 是否shuffle 161 | rounds = 30 # 实验次数 162 | verbose = 0 163 | 164 | # 获取模型 165 | model_name = 'BD LSTM' 166 | model = get_model(model_name, n_steps_in, n_steps_out) 167 | 168 | model_path = Path("model_hub") 169 | if not model_path.exists(): 170 | model_path.mkdir() 171 | 172 | # 读取数据 173 | file_path = r"./BTC-USD2019.csv" 174 | features = ['Close'] 175 | data = read_data(file_path, features) 176 | 177 | # 归一化 178 | norm_values, scaler = data_trasform(data.values) 179 | data_norm = pd.DataFrame(data=norm_values, index=data.index, columns=data.columns, copy=True) 180 | 181 | # 划分数据集 182 | ratio = 0.7 183 | data_len = data_norm.shape[0] 184 | train_set, test_set = data_norm.iloc[0:int(data_len * ratio), ], data_norm.iloc[int(data_len * ratio):, ] 185 | 186 | y_train = keras.utils.timeseries_dataset_from_array(train_set.values[n_steps_in:], 187 | targets=None, 188 | sequence_length=n_steps_out, 189 | batch_size=None) 190 | 191 | x_train = keras.utils.timeseries_dataset_from_array(train_set.values[:-n_steps_out], 192 | targets=None, 193 | sequence_length=n_steps_in, 194 | batch_size=None) 195 | 196 | y_test = keras.utils.timeseries_dataset_from_array(test_set.values[n_steps_in:], 197 | targets=None, 198 | sequence_length=n_steps_out, 199 | batch_size=None) 200 | 201 | x_test = keras.utils.timeseries_dataset_from_array(test_set.values[:-n_steps_out], 202 | targets=None, 203 | sequence_length=n_steps_in, 204 | batch_size=None) 205 | 206 | x_train = np.array(list(x_train.as_numpy_iterator())) 207 | y_train = np.array(list(y_train.as_numpy_iterator())) 208 | x_test = np.array(list(x_test.as_numpy_iterator())) 209 | y_test = np.array(list(y_test.as_numpy_iterator())) 210 | 211 | # 训练 212 | result = test_set.iloc[n_steps_in + n_steps_out - 1:] 213 | result.rename(columns={'Close': "Actual"}, inplace=True) 214 | # 反归一化 215 | result['Actual'] = data_trasform(result['Actual'].values.reshape(-1, 1), anti=True, scaler=scaler) 216 | 217 | train_history = None 218 | exp_result = pd.DataFrame({}, columns=['Train MINMAX RMSE', 'Test MINMAX RMSE', 'Train MAPE', 'Test MAPE']) 219 | # 收集评估指标 220 | columns = ['Round'] 221 | for step in range(1, n_steps_out + 1): 222 | columns.extend( 223 | [f'Train_RMSE_Step{step}', f'Test_RMSE_Step{step}', f'Train_MAPE_Step{step}', f'Test_MAPE_Step{step}']) 224 | metrics_df = pd.DataFrame(columns=columns) 225 | for round in range(rounds): 226 | print(f"Training: {round}/{rounds}") 227 | column_name = 'round' + str(round) 228 | 229 | # 保存模型 230 | model_save_name = Path(model_path, (model_name + str(round) + ".keras")) 231 | 232 | # 训练模型 233 | train_history = model.fit(x_train, y_train, batch_size=batch_size, shuffle=shuffle, epochs=epochs, 234 | verbose=verbose) 235 | x_train_pred = model.predict(x_train, batch_size=batch_size, verbose=verbose) 236 | x_test_pred = model.predict(x_test, batch_size=batch_size, verbose=verbose) 237 | 238 | model.save(model_save_name) 239 | # 保存预测结果 240 | for i in range(n_steps_out): 241 | column_name = f'round{round}_step{i + 1}' 242 | result[column_name] = data_trasform(x_test_pred[:, i].reshape(-1, 1), anti=True, scaler=scaler) 243 | result.to_excel(model_name + " result.xlsx") 244 | 245 | # 反归一化预测结果和实际数据 246 | x_train_pred_unnorm = data_trasform(x_train_pred.reshape(-1, n_steps_out), anti=True, scaler=scaler) 247 | y_train_unnorm = data_trasform(y_train.reshape(-1, n_steps_out), anti=True, scaler=scaler) 248 | x_test_pred_unnorm = data_trasform(x_test_pred.reshape(-1, n_steps_out), anti=True, scaler=scaler) 249 | y_test_unnorm = data_trasform(y_test.reshape(-1, n_steps_out), anti=True, scaler=scaler) 250 | 251 | # 计算并收集指标 252 | metrics_row = [round] 253 | train_rmse = eval_result(x_train_pred_unnorm, n_steps_out, y_train_unnorm, 0) 254 | test_rmse = eval_result(x_test_pred_unnorm, n_steps_out, y_test_unnorm, 0) 255 | train_mape = eval_result(x_train_pred_unnorm, n_steps_out, y_train_unnorm, 1) 256 | test_mape = eval_result(x_test_pred_unnorm, n_steps_out, y_test_unnorm, 1) 257 | for step in range(n_steps_out): 258 | metrics_row.extend([train_rmse[step], test_rmse[step], train_mape[step], test_mape[step]]) 259 | metrics_df.loc[len(metrics_df)] = metrics_row 260 | 261 | # 保存评估结果 262 | exp_result.loc[len(exp_result.index)] = [train_rmse, 263 | test_rmse, 264 | train_mape, 265 | test_mape] 266 | exp_result.to_excel(model_name + 'RMSEMAPE.xlsx') 267 | 268 | # 保存训练历史 269 | history = pd.DataFrame(train_history.history) 270 | model_history_path = model_name + " train history.xlsx" 271 | history.to_excel(model_history_path) 272 | 273 | # 绘图，专注于第一步 274 | fig_save_name = model_name + ' Prediction.png' 275 | data_draw = draw(result, fig_save_name, step=1, confidence=0.9) 276 | data_draw.to_excel(model_name + " draw data.xlsx") 277 | 278 | confidence_intervals_df = calculate_stepwise_confidence_intervals(metrics_df, n_steps_out, confidence=0.95) 279 | confidence_intervals_df.to_excel(model_name + '_ConfidenceIntervals.xlsx') 280 | 281 | 282 | if __name__ == '__main__': 283 | main() 284 | 285 | 286 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | # 读取数据 2 | import os 3 | import sys 4 | import pandas as pd 5 | import numpy as np 6 | from sklearn.preprocessing import MinMaxScaler 7 | from sklearn.model_selection import TimeSeriesSplit 8 | from tensorflow.keras.models import Sequential, Model 9 | from tensorflow.keras.layers import LSTM, Bidirectional, Dense, Conv1D, Flatten, MaxPooling1D, RepeatVector, \ 10 | TimeDistributed, LayerNormalization, Dropout, MultiHeadAttention, Input 11 | from tensorflow.keras.optimizers import Adam 12 | import statsmodels.api as sm 13 | from pmdarima import auto_arima 14 | from scipy import stats 15 | from statsmodels.tsa.arima.model import ARIMA 16 | import matplotlib.pyplot as plt 17 | 18 | def read_data(path, dim_type, gold_path=None, use_percentage=1): 19 | ''' 20 | 读取数据(详细说明) 21 | ''' 22 | df = pd.read_csv(path) 23 | data_len = df.shape[0] 24 | data = None 25 | if dim_type!='Multi': 26 | data = df[dim_type].values.reshape((data_len, 1)) 27 | else: 28 | # Multi 29 | df["Date"]=pd.to_datetime(df["Date"], format='%Y-%m-%d %H:%M:%S').dt.strftime('%Y-%m-%d') 30 | open_data = df["Open"].values.reshape((data_len, 1)) 31 | high_data = df["High"].values.reshape((data_len, 1)) 32 | low_data = df["Low"].values.reshape((data_len, 1)) 33 | close_data = df["Close"].values.reshape((data_len, 1)) 34 | if gold_path is not None: 35 | gold=pd.read_excel(gold_path) # 读取金价 36 | gold['Date']=gold['Date'].dt.strftime('%Y-%m-%d') 37 | df['Gold']=df['Date'] 38 | 39 | # calc the gold series 40 | df['Gold'] = df['Gold'].apply(lambda x: gold['Price'][x==gold['Date']].values[0] if x in gold['Date'].values else np.nan) 41 | # fillna using interpolating 42 | df['Gold'] = df['Gold'].interpolate(limit_direction="both") 43 | 44 | gold_data=df["Gold"].values.reshape((data_len, 1)) 45 | data = np.hstack((close_data, open_data, high_data, low_data, gold_data)) 46 | else: 47 | data = np.hstack((close_data, open_data, high_data, low_data)) 48 | return data[0:int(np.floor(data_len * use_percentage))], np.floor(data_len * use_percentage) 49 | 50 | def split_sequence(sequence, dim_type, n_steps_in, n_steps_out): 51 | X, y = list(), list() 52 | for i in range(len(sequence)): 53 | # find the end of the input pattern 54 | end_ix = i + n_steps_in 55 | # find the end of the output pattern 56 | out_end_ix = end_ix + n_steps_out 57 | # check if we are beyond the dataset 58 | if out_end_ix > len(sequence): 59 | break 60 | if dim_type == 'Multi': 61 | # gather input and output parts of the pattern 62 | seq_x = sequence[i:end_ix, 1:] 63 | seq_y = sequence[end_ix:out_end_ix, 0] 64 | else: 65 | seq_x, seq_y = sequence[i:end_ix], sequence[end_ix:out_end_ix] 66 | X.append(seq_x) 67 | y.append(seq_y) 68 | return np.array(X), np.array(y) 69 | 70 | def data_trasform(data, anti=False, scaler=None): 71 | ''' 72 | 说明以及例子 73 | MinMax data and anti MinMax data 74 | :param data: the data source 75 | :param model: MinMax and anti MinMax 76 | :param scaler: anti MinMax scaler 77 | :return: the transformed data 78 | 79 | ''' 80 | if not anti: 81 | # 归一化 82 | # 创建一个空字典来存储每一列的 scaler 83 | scalers = {} 84 | # 归一化数据的容器 85 | normalized_data = np.zeros_like(data) 86 | # 循环每一列 87 | for i in range(data.shape[1]): # data.shape[1] 是列的数量 88 | # 为每一列创建一个新的 MinMaxScaler 89 | scaler = MinMaxScaler() 90 | # 将列数据调整为正确的形状，即(-1, 1) 91 | column_data = data[:, i].reshape(-1, 1) 92 | # 拟合并转换数据 93 | normalized_column = scaler.fit_transform(column_data) 94 | # 将归一化的数据存回容器中 95 | normalized_data[:, i] = normalized_column.ravel() 96 | # 存储scaler以便后续使用 97 | scalers[i] = scaler 98 | # 现在 normalized_data 是完全归一化的数据 99 | # scalers 字典包含每一列的 MinMaxScaler 实例 100 | return normalized_data, scalers 101 | else: 102 | # 反归一化 103 | # 如果data是三维数组，去除最后一个维度 104 | if data.ndim == 3 and data.shape[2] == 1: 105 | data = data.squeeze(axis=2) 106 | 107 | restored_data = np.zeros_like(data) 108 | for i in range(data.shape[1]): # 遍历所有列 109 | column_data = data[:, i].reshape(-1, 1) 110 | restored_data[:, i] = scaler.inverse_transform(column_data).ravel() 111 | return restored_data 112 | 113 | def create_transformer_model(input_seq_length, output_seq_length, num_features, d_model, num_heads, ff_dim, 114 | num_transformer_blocks, dropout_rate=0.1): 115 | inputs = Input(shape=(input_seq_length, num_features)) 116 | 117 | x = Dense(d_model)(inputs) 118 | 119 | for _ in range(num_transformer_blocks): 120 | attn_output = MultiHeadAttention(num_heads=num_heads, key_dim=d_model)(x, x) 121 | attn_output = Dropout(dropout_rate)(attn_output) # Dropout after attention 122 | x = LayerNormalization(epsilon=1e-6)(x + attn_output) 123 | 124 | ff_output = Dense(ff_dim, activation="relu")(x) 125 | ff_output = Dropout(dropout_rate)(ff_output) # Dropout after first dense layer 126 | ff_output = Dense(d_model)(ff_output) 127 | 128 | x = LayerNormalization(epsilon=1e-6)(x + ff_output) 129 | 130 | outputs = Dense(output_seq_length)(x[:, -1, :]) # We take the last step's output for forecasting 131 | model = Model(inputs, outputs) 132 | return model 133 | 134 | def create_model(model_type, n_features, n_steps_in, n_steps_out): 135 | ''' 136 | create model 137 | :param model_type: LSTM,BD LSTM(bidirectional LSTM),ED LSTM(Encoder-Decoder LSTM),CNN 138 | :param n_features: 139 | :param n_steps_in: 140 | :param n_steps_out: 141 | :return: the created model 142 | ''' 143 | model = Sequential() 144 | adam_optimizer = Adam(learning_rate=0.001) 145 | if model_type == 'LSTM': 146 | # LSTM 147 | model.add(LSTM(100, activation='sigmoid', return_sequences=True, input_shape=(n_steps_in, n_features))) 148 | model.add(LSTM(100, activation='sigmoid')) 149 | model.add(Dense(n_steps_out)) 150 | 151 | elif model_type == 'BD LSTM': 152 | # bidirectional LSTM 153 | model.add(Bidirectional(LSTM(50, activation='sigmoid'), input_shape=(n_steps_in, n_features))) 154 | model.add(Dense(n_steps_out)) 155 | 156 | elif model_type == 'ED LSTM': 157 | # Encoder-Decoder LSTM 158 | # Encoder 159 | model.add(LSTM(100, activation='sigmoid', input_shape=(n_steps_in, n_features))) 160 | # Connector 161 | model.add(RepeatVector(n_steps_out)) 162 | # Decoder 163 | model.add(LSTM(100, activation='sigmoid', return_sequences=True)) 164 | model.add(TimeDistributed(Dense(1))) 165 | 166 | elif model_type == 'CNN': 167 | # CNN 168 | model.add(Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(n_steps_in, n_features))) 169 | model.add(MaxPooling1D(pool_size=2)) 170 | model.add(Flatten()) 171 | model.add(Dense(20, activation='relu')) 172 | model.add(Dense(n_steps_out)) 173 | 174 | elif model_type == 'Convolutional LSTM': 175 | # Convolutional LSTM 176 | model.add(Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(n_steps_in, n_features))) 177 | model.add(LSTM(20, activation='relu', return_sequences=False)) 178 | model.add(Dense(20, activation='relu')) 179 | model.add(Dense(n_steps_out)) 180 | 181 | elif model_type == 'Transformer': 182 | model = create_transformer_model(n_steps_in, n_steps_out, n_features, d_model=64, 183 | num_heads=12, ff_dim=64, num_transformer_blocks=3) 184 | 185 | elif model_type == 'MLP': 186 | # 多层感知机 (MLP) 187 | model.add(Dense(20, activation='relu', input_shape=(n_steps_in, n_features))) 188 | model.add(Flatten()) 189 | model.add(Dense(20, activation='relu')) 190 | model.add(Dense(n_steps_out)) 191 | 192 | elif model_type == 'ARIMA': 193 | if n_features != 1: 194 | print("ARIMA model only supports univariate time series data") 195 | print("ARIMA model has no parameter n_steps_in") 196 | return None 197 | model = 'ARIMA' 198 | return model 199 | 200 | else: 201 | print("no model") 202 | model.compile(optimizer=adam_optimizer, loss='mse') 203 | return model 204 | 205 | 206 | def train_and_forecast(model, n_features, dim_type, data_X, data_Y, n_steps_in, n_steps_out, ech): 207 | # 训练模型 208 | # 隐藏输出 209 | sys.stdout = open(os.devnull, 'w') 210 | sys.stderr = open(os.devnull, 'w') 211 | 212 | X, y = split_sequence(data_X, dim_type, n_steps_in, n_steps_out) 213 | # 对于多维数据，调整最后一个维度为特征数 214 | X = X.reshape((X.shape[0], X.shape[1], n_features)) 215 | 216 | ####################################################################################### 217 | if model == 'ARIMA': 218 | # 检查数据是否平稳 219 | fig = plt.figure(figsize=(12, 8)) 220 | ax1 = fig.add_subplot(211) 221 | fig = sm.graphics.tsa.plot_acf(data_X.squeeze(), lags=40, ax=ax1) 222 | ax2 = fig.add_subplot(212) 223 | fig = sm.graphics.tsa.plot_pacf(data_X, lags=40, ax=ax2) 224 | 225 | # # 自动确定 ARIMA 模型的参数 226 | # auto_model = auto_arima(data_X, seasonal=False, trace=True, error_action='ignore', suppress_warnings=True) 227 | # # 输出最佳 ARIMA 模型的参数 228 | # print(auto_model.summary()) 229 | 230 | # 使用最佳参数拟合 ARIMA 模型 231 | # ARIMA 模型只接受单变量时间序列，这里假设 data_X 和 data_Y 是一维数组 232 | # order = auto_model.order 233 | order = (6,1,6) 234 | arma_model = ARIMA(data_X, order=order) 235 | model_fit = arma_model.fit() 236 | # 拟合结果 237 | fit_result = model_fit.fittedvalues 238 | # 使用模型进行滚动预测 239 | history = list(data_X) 240 | test_result = [] 241 | for t in range(len(data_Y)): 242 | model = ARIMA(history, order=order) 243 | model_fit = model.fit() 244 | output = model_fit.forecast(n_steps_out) 245 | test_result.append(output) 246 | history.append(data_Y[t]) # 更新历史数据 247 | test_result = np.array(test_result) 248 | fit_result = fit_result.reshape(len(fit_result), 1) 249 | return fit_result, test_result 250 | ####################################################################################### 251 | 252 | # 训练模型 253 | model.fit(X, y, epochs=ech, batch_size=32, verbose=1) 254 | 255 | # 拟合结果 256 | fit_result = [] 257 | for index, ele in enumerate(X): 258 | print(f'Fitting {index}th data') 259 | pred = model.predict(ele.reshape((1, n_steps_in, n_features))) 260 | fit_result.append(pred) 261 | fr = np.array(fit_result) 262 | fit_result = fr.reshape(len(fit_result), n_steps_out) 263 | # 测试结果 264 | test_x, test_y = split_sequence(data_Y, dim_type, n_steps_in, n_steps_out) 265 | test_x = test_x.reshape((test_x.shape[0], test_x.shape[1], n_features)) 266 | test_result = [] 267 | for index, ele in enumerate(test_x): 268 | print(f'Predicting {index}th data') 269 | pred = model.predict(ele.reshape((1, n_steps_in, n_features))) 270 | test_result.append(pred) 271 | tr = np.array(test_result) 272 | test_result = tr.reshape(len(test_result), n_steps_out) 273 | 274 | # 恢复输出 275 | sys.stdout = sys.__stdout__ 276 | sys.stderr = sys.__stderr__ 277 | return fit_result, test_result 278 | 279 | def eval_result(result, n_steps_out, target, mode): 280 | ''' 281 | evaluate the modl resule 282 | :param result:the model result 283 | :param n_steps_out:the days you predict 284 | :param target:the ground-true 285 | :param mode:the type of evaluation(you can choose 0：rmse,1：mape) 286 | :return:the evaluation result 287 | ''' 288 | if mode==0: 289 | # return rmse result 290 | # 归一化 291 | result, _ = data_trasform(result) 292 | target, _ = data_trasform(target) 293 | # 下面需要修改 294 | rmse = [] 295 | for i in range(n_steps_out): 296 | rmse.append(np.sqrt(np.mean((result[:, i] - target[:, i]) ** 2))) 297 | return rmse 298 | 299 | elif mode==1: 300 | # return MAPE result 301 | result = result + 0.0000001 302 | target = target + 0.0000001 303 | mape = [] 304 | for i in range(n_steps_out): 305 | mape.append(np.mean(np.abs((target[:, i] - result[:, i]) / target[:, i])) * 100) 306 | return mape 307 | else: 308 | return None 309 | 310 | 311 | def main(): 312 | print("start") 313 | # -----------------parameters----------------- 314 | model_hub = ['LSTM', 'BD LSTM', 'ED LSTM', 'CNN', 'Convolutional LSTM', 'Transformer', 'MLP', 'ARIMA'] 315 | file_path = r"./coin_Bitcoin.csv" 316 | dim_type = 'Close' # 'Multi' or 'Open', 'High', 'Low', 'Close', 'Marketcap' (选取数据的维度或类型) 317 | gold_path = r"./GoldPrice.xlsx" 318 | use_percentage = 1 # 使用的数据百分比(=1就是全部数据) 319 | 320 | n_steps_in = 6 # 输入步长 321 | n_steps_out = 5 # 输出步长 322 | 323 | percentage = 0.7 # 训练集百分比 324 | epochs = 100 # 迭代次数 325 | rounds = 30 # Number of exp 326 | 327 | # ---------------get data--------------- 328 | 329 | data, data_len = read_data(file_path, dim_type, gold_path, use_percentage) 330 | # data, scalers = MinMaxdata(data) # 归一化数据 331 | data, scalers = data_trasform(data) 332 | # split into train and test 333 | train_set = data[0:int(np.floor(data_len * percentage))] # 训练集 334 | test_set = data[int(np.floor(data_len * percentage)):] # 测试集 335 | 336 | # ----------保存、重新读取数据集 337 | 338 | # ------------------------- 339 | 340 | # define the used features 341 | # used gold price or no used 342 | n_features = len(train_set[0]) - 1 if len(train_set[0]) > 1 else 1 343 | 344 | # ------------------create model and prediction--------------- 345 | model_type = 'Convolutional LSTM' # Encoder-Decoder 346 | 347 | 348 | exp_result=pd.DataFrame({},columns=['Train MINMAX RMSE', 'Test MINMAX RMSE', 'Train MAPE', 'Test MAPE']) 349 | 350 | 351 | for round in range(rounds): 352 | print(f"the {round}-th exp, total:{rounds} rounds") 353 | Model = create_model(model_type, n_features, n_steps_in, n_steps_out) 354 | 355 | train_result, test_result = train_and_forecast(Model, n_features, dim_type, train_set, test_set, n_steps_in, 356 | n_steps_out, epochs) 357 | 358 | # ----------------------evaluation-------------------- 359 | train_result = data_trasform(train_result, True, scalers[0]) # 反归一化 360 | test_result = data_trasform(test_result, True, scalers[0]) # 反归一化 361 | 362 | _, train = split_sequence(train_set, dim_type, n_steps_in, n_steps_out) 363 | train = data_trasform(train, True, scalers[0]) # 反归一化 364 | _, test = split_sequence(test_set, dim_type, n_steps_in, n_steps_out) 365 | test = data_trasform(test, True, scalers[0]) # 反归一化 366 | 367 | # calc the rmse 368 | per_n_steps_out = n_steps_out 369 | if Model == 'ARIMA': 370 | train = train_set 371 | train = data_trasform(train, True, scalers[0]) 372 | test_result = test_result[0:len(test), :] 373 | n_steps_out = 1 374 | 375 | train_minmax_rmse = eval_result(train_result, n_steps_out, train, 0) 376 | if Model == 'ARIMA': 377 | n_steps_out = per_n_steps_out 378 | test_minmax_rmse = eval_result(test_result, n_steps_out, test, 0) 379 | 380 | print('Train MINMAX RMSE:', train_minmax_rmse) 381 | print('Test MINMAX RMSE:', test_minmax_rmse) 382 | 383 | # calc the MAPE 384 | if Model == 'ARIMA': 385 | n_steps_out = 1 386 | train_MAPE = eval_result(train_result, n_steps_out, train, 1) 387 | if Model == 'ARIMA': 388 | n_steps_out = per_n_steps_out 389 | test_MAPE = eval_result(test_result, n_steps_out, test, 1) 390 | print('Train MAPE:', train_MAPE) 391 | print('Test MAPE:', test_MAPE) 392 | 393 | # save result 394 | exp_result.loc[len(exp_result.index)] = [train_minmax_rmse, 395 | test_minmax_rmse, 396 | train_MAPE, 397 | test_MAPE] 398 | exp_result.to_excel("results.xlsx") 399 | 400 | 401 | # 绘图 402 | if n_steps_out == 1: 403 | # 拟合结果 404 | plt.figure(figsize=(15, 6)) 405 | plt.plot(train_result, label='Train') 406 | plt.plot(train, label='Actual') 407 | plt.title(f'{model_type} Train Results') 408 | plt.xlabel('Time Steps') 409 | plt.ylabel('Close Price') 410 | plt.legend() 411 | plt.show() 412 | 413 | # 测试结果 414 | plt.figure(figsize=(15, 6)) 415 | plt.plot(test_result, label='Predicted') 416 | plt.plot(test, label='Actual') 417 | plt.title(f'{model_type} Test Results') 418 | plt.xlabel('Time Steps') 419 | plt.ylabel('Close Price') 420 | plt.legend() 421 | plt.show() 422 | 423 | 424 | if __name__ == '__main__': 425 | main() 426 | 427 | -------------------------------------------------------------------------------- /results/Bitcoin/Bitcoin_Univariate_Mape.csv: -------------------------------------------------------------------------------- 1 | ,LSTM Train MAPE,LSTM Test MAPE,ED-LSTM Train MAPE,ED-LSTM Test MAPE,BD-LSTM Train MAPE,BD-LSTM Test MAPE,CNN Train MAPE,CNN Test MAPE,Covn-LSTM Single Train MAPE,Covn-LSTM Test 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7.023326877358857, 7.932733893582309, 8.315857933292731, 9.337055964751565]" 7 | 5,"[23.945394824746273, 15.768366461229533, 31.80132072349257, 29.912913333346737, 24.525106455974807]","[6.33975998359644, 6.085359049134024, 7.600315206987503, 8.044397782224493, 8.04926393863892]","[59.35941826389768, 62.35937174071662, 59.69835030803693, 64.29187278489027, 62.78240228230471]","[7.408988561255117, 8.049938834326905, 8.66587719057918, 9.332288855753097, 9.521725500681203]","[9.163190460211542, 23.037365943871496, 19.779223966511267, 28.980127440282853, 29.022423579148963]","[3.011236358417384, 5.408422155226489, 5.6888520550533395, 7.124693452405544, 7.559355044705425]","[20.707323490181047, 15.53480960942681, 16.65482486578405, 16.566683902169547, 14.28038873728362]","[8.641578973425126, 9.293849244816446, 9.703096817964033, 10.429116156184522, 11.100086620707037]","[9.375131592009952, 11.696994998651348, 13.764997178710686, 9.68281737972175, 12.628583357321189]","[4.599138844202396, 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26.254832760420516]","[3.4774919118767755, 4.4396203916604025, 5.318123944745564, 6.113366757895116, 6.674304091421229]","[8.228589079546698, 30.624440271972354, 28.087781671896867, 26.68250685913635, 19.57828332561659]","[3.14111550272837, 6.3550846233024245, 6.653934619531389, 7.011728052770934, 7.04216470289509]","[8.160532804281651, 6.644165138720935, 8.849676784594578, 9.423356434594538, 10.656252217417586]","[5.648593878235529, 6.753482092218864, 8.139817410324442, 8.871850025679386, 9.283568620293217]","[17.38361758535038, 17.75387348752193, 19.23332343877797, 17.077697309190526, 17.459618174288543]","[5.133700382582273, 5.488666117713042, 6.031948218433765, 6.609214731964995, 7.429256882376559]","[18.96051265200005, 9.801900388313122, 23.58386225539295, 12.368040225152994, 19.74971669196767]","[5.10729029790815, 5.239697690624014, 7.813984057835986, 7.915181248282647, 8.881813020136024]" 11 | 9,"[8.661264078382782, 11.062250033419685, 12.902120690954646, 11.18205041203814, 16.15448116580919]","[2.978086515884238, 4.172464200696992, 5.093816493798648, 5.808396576250476, 6.549515696841818]","[3.16583357870084, 8.600474756462479, 6.547229762718701, 8.901279757600976, 8.85492534708364]","[2.854159110082299, 3.9762800006692753, 4.858401394629089, 5.643637222697398, 6.362895747629871]","[19.673728946724786, 20.66047513708978, 16.554507719957698, 40.20412268932392, 43.09843597293608]","[3.3392934983465334, 4.337353272482389, 5.0684031827455716, 7.287080139637609, 8.338319372918823]","[9.094165545713254, 8.412916397628974, 8.703765515959118, 7.225811472594078, 10.310573717487339]","[6.635950790971494, 7.389605100798937, 8.59150101675544, 9.090186284582494, 9.649371175032988]","[8.193850997518869, 7.908155999390363, 10.970626460733566, 12.839448788194385, 10.260102283544391]","[3.510783600927848, 4.711180983538746, 5.614341751676104, 6.456178805290329, 7.326177617180594]","[26.786850807921425, 25.523259342950038, 32.94386052069347, 27.79344192667149, 38.367748762598424]","[11.492250580614034, 11.99475340317591, 12.557248177372484, 11.596913818053189, 12.72392771302269]" 12 | 10,"[40.10355949895706, 38.53619758266683, 30.703895199776817, 37.933881683876, 36.8753467630861]","[6.723956813640491, 7.220230710754076, 7.140127159054104, 8.351740900828446, 9.119581523494684]","[9.00546052708678, 13.545925767197545, 14.01824014615309, 13.335581983218697, 14.115542045020923]","[4.047793106062363, 5.7980744592377, 6.1889366256140725, 6.823377657935241, 7.55430767202169]","[11.037604062960519, 13.803262376533363, 10.538307304999499, 25.65028089966183, 18.756443493333787]","[3.571219857061595, 4.698113307821907, 5.200119215641145, 6.989636841739127, 6.96159250159179]","[20.85882011018319, 10.63526154114258, 8.281518747533006, 16.95241496930976, 12.283279459992064]","[6.133480982844507, 6.053455307527212, 7.061641980807362, 7.729858892409476, 8.559463481894186]","[8.07980890737177, 7.7142132638927885, 12.229842219147448, 13.0613640187088, 14.611207135735418]","[3.8629484996841517, 4.823546727198962, 5.9420535719661185, 6.501114888425436, 7.631474672492369]","[10.108583550227845, 16.468798152102956, 10.869483387558798, 23.931064617186667, 14.799348068396023]","[4.5868441739263295, 6.251793684916447, 6.456063551713344, 7.842532008453332, 8.026373259601838]" 13 | 11,"[21.83250347070175, 14.323460754870867, 22.078348789282266, 22.074435518981105, 24.57311506300289]","[3.7753566067733253, 4.285127221207594, 5.550690197835097, 6.258341103420061, 6.977913666071872]","[23.066584273861586, 24.501954521749987, 24.620235021601236, 24.513168562825808, 24.887622624127946]","[5.812192049738176, 7.273518521574861, 7.620989062595676, 8.239641432003513, 8.861294748256135]","[20.942108594407486, 19.72143281371116, 7.269415902490748, 16.497837197984836, 14.765715011457244]","[4.394984017738201, 5.116505709098186, 4.941390535739122, 6.1666605757410595, 6.718428606237453]","[9.565685853073127, 14.925049225194694, 15.878367346863055, 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34.80640453930138, 46.66826019483229]","[2.9975481874379506, 5.4273685835410435, 6.4074345181531145, 7.140580283826719, 8.917051465041384]","[17.75872904889542, 10.648290931064723, 13.879081952276175, 13.756324102186479, 12.80438999972924]","[5.779554458365185, 6.169865422535372, 7.373516883687057, 7.965351610312865, 8.656156812522335]","[18.720087332645196, 10.30459671884036, 6.9836621856352945, 15.847958888718845, 13.101564607146098]","[3.806931536542143, 4.576203697627794, 5.302734703341506, 6.895246998545207, 7.229216893337992]","[9.246331505541885, 8.50761655692722, 6.983994463385552, 8.025046999255963, 8.27344535515012]","[4.643837728221023, 5.318344729493271, 5.976011604893853, 6.630417951563758, 7.406478067204465]" 15 | 13,"[24.49718154868934, 21.024481866889747, 34.58381118974548, 24.06007357619981, 20.598359859055073]","[4.103209058095894, 4.752295115809449, 6.467263088057981, 6.375561411877087, 6.810413517460626]","[42.02369498831133, 39.64429286404697, 39.5664808527177, 39.85276045494041, 40.066250405570024]","[7.481640632502935, 8.60564127150916, 9.033537058340169, 9.635743554994228, 10.208288641127385]","[37.642737401919746, 44.03284828293217, 39.636835292012755, 40.46217586324303, 34.92129497339689]","[6.840485991332653, 8.14542096709301, 8.124979766504108, 8.758372227628069, 8.474502345190201]","[14.66437432227224, 24.82586742319875, 21.23024271961719, 22.854379500392277, 18.780440540257178]","[8.955805793113502, 10.600380856931844, 10.459906129370108, 10.995394589954929, 12.357824092609032]","[18.27334854280352, 14.368128074801023, 11.888606439379835, 14.813481073566706, 18.106243169710886]","[3.997168016573073, 5.020528216667429, 5.633571004399252, 6.792604270618605, 7.537466039770216]","[20.6756063365417, 26.001846522007277, 34.21801605046753, 29.46686541307198, 30.68096680894093]","[5.774874719013303, 6.161286069711121, 6.857330764191808, 7.315567546719339, 7.826997685397869]" 16 | 14,"[20.718342016608915, 17.06917117408036, 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7.427443386557305]","[17.04824364887685, 17.841865881479688, 19.732607914063138, 16.63632104371829, 18.090363123862055]","[4.48212755761548, 5.564897932267437, 6.0567821259489385, 6.626387897312136, 7.353907804584662]","[4.360019008984183, 7.0016282368068605, 6.382480611553533, 25.746660837118547, 44.86592415629319]","[3.0430968091984685, 4.336549672221654, 5.506576892517027, 7.616823867295702, 9.744525389493363]","[15.541660743938943, 8.327317824324757, 10.239645218337762, 12.482961926922833, 14.695636696255843]","[9.698360494153482, 10.015850007225332, 11.308043826286172, 12.162731068961982, 12.365211910923653]","[8.809905437088416, 5.384624748509929, 6.686907198934748, 11.457914860070245, 12.17499089617314]","[8.923029037212924, 7.397850521715251, 11.19655199557661, 13.627665188618899, 16.924628125598094]","[13.29070482300001, 10.203273272372511, 15.329219799378363, 21.06209240238457, 15.769132546141169]","[8.313337032075623, 8.874363656045634, 9.907757328506191, 10.809515382037734, 11.848619161304752]" 8 | 6,"[22.915168842948454, 12.430950823707827, 17.99734356885644, 7.8904583959515024, 11.814399938912047]","[5.486009794815152, 5.361840932389123, 5.953718492700914, 5.962452113672524, 6.797369080908442]","[5.771698747402044, 7.129289390900828, 6.999797380423196, 9.30282647814725, 9.34381749489002]","[3.3807073596691253, 4.280242785225674, 5.1725656551461645, 5.858367932445089, 6.47009733521421]","[20.26581388275233, 10.308205214501722, 21.78818407427556, 30.987721267751052, 18.951770181436068]","[3.7949859755582667, 4.4489649478085616, 7.027115081720507, 8.469175955538939, 7.886888030193457]","[12.566357309404863, 11.52466672092775, 12.786687036506713, 16.903330167669512, 13.546019821564917]","[10.023425386115429, 10.856267168819208, 11.856263250665986, 13.460235627293955, 12.667986986266886]","[18.456479853201515, 12.33767058222725, 6.881749208029773, 8.412777363293355, 14.652651727271612]","[9.095153394159333, 7.534343906580723, 11.786920820774546, 14.3527573200392, 18.596798159518578]","[16.524275088565428, 17.382372304649866, 12.843710455883087, 14.547565687790499, 23.23720755663194]","[9.232469451264834, 11.114585598882751, 10.68302641472834, 10.746908358189437, 11.385661205355659]" 9 | 7,"[6.06797820915265, 15.714642083968542, 16.584506369102424, 11.375560143495319, 21.154498170064855]","[3.7673796786153124, 5.3788530530554945, 6.156000646671299, 6.930550971534105, 8.013369873519133]","[17.13596672099168, 13.36065761086516, 13.664799840846767, 15.480625953924937, 15.650605332004764]","[3.4620913489656466, 4.437021819105883, 5.22243743657544, 5.906040780331521, 6.5022028834869365]","[4.950977949315784, 6.602713180553879, 15.932139554203175, 13.939343493920262, 13.342538842977556]","[3.238106543795084, 4.533881302552254, 5.488179597492065, 6.279056891887609, 7.180599978002378]","[8.035658814985519, 11.859906734905747, 8.86487105468526, 10.666752136261405, 11.170022087777706]","[8.65942202628224, 9.899475018746676, 10.852210727650954, 11.909071156771912, 11.732899095923848]","[13.019304447835514, 5.722647551312725, 7.7831808701822265, 11.902981482290809, 7.739349335779042]","[10.214630895670354, 8.97120036464021, 13.731295396240325, 16.684867280141983, 21.110069989675353]","[24.827818463255426, 33.211471493570855, 27.863195973502503, 22.867411975284146, 17.543025832313205]","[8.746220660120821, 9.742102373749098, 10.357104490619568, 10.74682299632518, 11.229747474388024]" 10 | 8,"[27.301070592445996, 13.38045020692309, 12.416043887927803, 10.518713799445687, 10.267282525624633]","[6.374151048551909, 5.938459414150986, 6.60603342262121, 7.372947060412736, 8.069274914364302]","[6.927101125083094, 10.276775381725418, 10.543478121327098, 11.575284942195777, 12.037342235665829]","[3.5124656036159037, 4.4268736007116, 5.147100283764995, 5.817627471815426, 6.404452119953969]","[11.931227534546974, 10.075336554018552, 21.628635005742137, 13.80008083039236, 16.74294757711096]","[3.6212035883222145, 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26.30585981935521]","[6.559187379094082, 7.014268825321864, 8.475200791816196, 9.202777018625467, 10.001901658371045]","[5.561732681301997, 7.694778545541481, 8.08244003052355, 8.694369037248805, 9.232181986871337]","[4.031974388628652, 5.315540687279176, 6.025540183625282, 6.728223610402346, 7.325628249004169]","[9.910804872786688, 7.861679188197254, 6.601065980182548, 14.716105326565085, 16.07369939903169]","[3.3793558859529247, 4.288156529741329, 5.815974351795848, 7.408449445429617, 8.152935753514477]","[15.640870131733694, 19.604489404127104, 12.883097017544392, 11.149555976905852, 12.703966673259043]","[10.174535921099473, 11.758137851401301, 12.520184184331306, 13.104825954757734, 13.545026048903052]","[5.71021521401484, 5.2726140255410785, 9.618224769512866, 11.91858502298501, 9.014733547565239]","[16.19405147394452, 13.034665528662273, 12.893146898400776, 16.429607930734548, 21.314485710042977]","[24.087063166535064, 21.138114360263422, 19.376066117707637, 13.902449403471598, 14.258185889453406]","[10.46043271620212, 11.166351651106423, 12.069060039602714, 13.154978732694417, 13.738944286611929]" 13 | 11,"[7.285566650379895, 14.136712605702806, 7.428125981120338, 11.482624477911429, 7.987728626818279]","[3.729181109757915, 5.456175935672087, 6.737201272477725, 7.905361387251414, 9.006369184328326]","[27.166475613281406, 25.17058518101602, 26.048203624970046, 26.427662135873348, 26.763804973926337]","[5.45273896933727, 6.729958771274429, 7.504880347400503, 8.237107735957828, 8.846004214308614]","[8.184253388402682, 5.318691042365951, 21.020143695855715, 8.60014861925996, 8.582822299282993]","[3.2977650777412637, 4.578098971786947, 5.927872834994225, 6.758292683626927, 7.599752861364251]","[7.911191758357908, 8.651584893159606, 10.096048237380506, 10.040453417347624, 13.218687163407264]","[9.788196001524268, 10.677018257278132, 12.160316045739325, 12.764813609003403, 13.62336750861094]","[13.39512667236423, 9.310098988739586, 8.531142574654591, 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12.1657890501553, 13.916192284572345, 11.963607263800407]","[10.5151533212564, 11.430813943354327, 12.42148651477379, 13.070380145386363, 14.077731284907918]","[7.513630516559807, 6.324817031989767, 11.202154900099885, 8.632057704584122, 7.269536676260308]","[17.625621118100977, 11.595379837682698, 19.172073095352324, 24.31743499034389, 35.31982403253403]","[7.543600605646028, 9.729601870890136, 7.904130462732255, 8.936375363457874, 9.72151601985273]","[9.707146543433478, 10.757490534294638, 11.470529750908701, 12.086419281132974, 12.948750373891842]" 15 | 13,"[18.77144021670852, 14.625680076883828, 9.267154539123913, 7.931949272867989, 8.287391067340497]","[4.656169050491397, 5.5263858124263034, 6.539921250334111, 7.533667583699809, 8.240222625841588]","[6.079148227031127, 7.385803133298344, 7.733819294708115, 8.410633294808028, 9.003552826780703]","[4.084508660497361, 5.819429221012617, 6.9516228970659215, 7.843176761943346, 8.549326956716042]","[40.08061411362841, 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15,"[26.84069199131563, 22.284005343740894, 18.42775195054157, 15.513363498160244, 9.726764559862017]","[4.1473398385747835, 6.184892695958225, 7.682053027033793, 9.078629175008265, 10.693417947386996]","[9.500789270887154, 11.742990445680446, 13.983931794468962, 14.916401146968369, 15.693792244217452]","[4.912270022239092, 6.716811348043307, 8.680171990768027, 10.361233277925667, 11.53327197701529]","[37.184438531150995, 29.235916105003856, 17.412490902087175, 9.019579913116257, 18.898601957739697]","[5.378307455833529, 7.186147103280015, 8.303334108245469, 9.376912556980747, 10.68420942386474]","[11.617822985736009, 12.008133176222453, 11.74295166689214, 13.146980750728238, 12.465327157616372]","[6.092889263821914, 7.201750362588723, 8.34239656493223, 9.738980142470737, 10.84234226599849]","[8.869997957307195, 14.869559029062332, 8.088986833991774, 8.913444082373587, 17.62027685801095]","[4.766478346552267, 6.7582220373775375, 8.004698739209319, 10.044011259899733, 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