[英]Statsmodel ARIMA prediction mismatch
我已經編寫了以下代碼來使用ARIMA的統計模型進行數據預測,但是我的結果與實際數據不匹配,並且在前幾次預測在圖形上給出一條水平直線后,預測值幾乎保持不變。
如果由於我使用了d = 2而預測是用於二階微分階數,那么對於相同模型,我如何獲得原始數據的預測。
arima_mod = sm.tsa.ARIMA(df, (1,2,0)).fit()
print(arima_mod.params)
print(arima_mod.summary())
predict_workshop = arima_mod.predict('2011-04-01', '2011-05-30',dynamic=True)
print(predict_workshop)
實際數據
2011-04-01 356.839
2011-04-02 363.524
2011-04-03 332.864
2011-04-04 336.228
2011-04-05 264.749
2011-04-06 321.212
2011-04-07 384.382
2011-04-08 273.250
2011-04-09 307.062
2011-04-10 326.247
2011-04-11 222.521
2011-04-12 135.326
2011-04-13 374.953
2011-04-14 329.583
2011-04-15 358.853
2011-04-16 343.169
2011-04-17 312.086
2011-04-18 339.302
2011-04-19 300.534
2011-04-20 367.166
2011-04-21 178.670
2011-04-22 320.823
2011-04-23 349.995
2011-04-24 323.120
2011-04-25 331.665
2011-04-26 352.993
2011-04-27 359.253
2011-04-28 308.281
2011-04-29 329.357
2011-04-30 301.873
預測值
2011-04-01 -50.693560
2011-04-02 30.715553
2011-04-03 -19.081318
2011-04-04 11.378766
2011-04-05 -7.253263
2011-04-06 4.143701
2011-04-07 -2.827670
2011-04-08 1.436625
2011-04-09 -1.171787
2011-04-10 0.423744
2011-04-11 -0.552221
2011-04-12 0.044764
2011-04-13 -0.320404
2011-04-14 -0.097036
2011-04-15 -0.233667
2011-04-16 -0.150092
2011-04-17 -0.201214
2011-04-18 -0.169943
2011-04-19 -0.189071
2011-04-20 -0.177371
2011-04-21 -0.184528
2011-04-22 -0.180150
2011-04-23 -0.182828
2011-04-24 -0.181190
2011-04-25 -0.182192
2011-04-26 -0.181579
2011-04-27 -0.181954
2011-04-28 -0.181724
2011-04-29 -0.181865
2011-04-30 -0.181779
一點提示。 您應該在期間11,12,21包括一個虛擬干預變量。無需將此模型加倍加倍。 只需攔截和3個干預變量即可。 Y(T)= 332.20
+ [X1(T)] [(-196.87)]:PULSE 12 + [X2(T)] [(-153.53)]:PULSE 21 + [X3(T)] [(-109.68)]:PULSE 11 + + [在)]
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