python混沌時間序列分析_用Python進行時間序列分析

我想确定趨勢序列B對趨勢序列A的估計有多好。我用OLS嘗試過，但很明顯殘差是自相關的。我試圖用Cochrane-Orcutt過程(https://onlinecourses.science.psu.edu/stat501/node/360)來糾正，但這并沒有糾正自相關。我嘗試了使用不同rho值的python statsmodels GSLAR函數，但也沒有成功。在

我錯過了什麼？回歸分析是正确的分析方法嗎？什麼是替代品？在

資料如下：import pandas as pd

dataA = [0.02921, 0.02946, 0.02971, 0.02996, 0.03021, 0.03042, 0.03063,

0.03083, 0.031, 0.03117, 0.03129, 0.03142,

0.0315, 0.03146, 0.03142, 0.03142, 0.03138, 0.03129, 0.03117, 0.03104,

0.03096, 0.03083, 0.03067, 0.0305, 0.03042, 0.03042, 0.03042, 0.03042,

0.03046, 0.03058, 0.03075, 0.03087, 0.031, 0.03117, 0.03137, 0.03158,

0.03175, 0.03196, 0.03221, 0.03242, 0.03258, 0.03271, 0.03279, 0.03292,

0.03304, 0.03312]

dataB = [0.28416, 0.28756, 0.29716, 0.30777, 0.31047, 0.30262, 0.29666,

0.28918, 0.28008, 0.28037, 0.27909, 0.2738, 0.28378, 0.29538, 0.2927,

0.29232, 0.28845, 0.27793, 0.27858, 0.29067, 0.29573, 0.29336, 0.28964,

0.28601, 0.273, 0.26278, 0.26786, 0.27156, 0.27272, 0.28691, 0.30556,

0.3109, 0.31243, 0.31083, 0.31534, 0.32455, 0.33221, 0.33714, 0.33397,

0.32347, 0.31899, 0.31567, 0.30213, 0.29288, 0.29132, 0.29346]

daterange = pd.date_range(start='2012-07-31', end='2016-04-30',freq='M')

A = pd.Series(dataA, daterange)

B = pd.Series(dataB, daterange)

資料a和資料B來自季節分解(加法模型)：

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