在python中使用ARMA/ARIMA的線性迴歸模型

Question

有人能給我一個關於如何使用ARMA和線性迴歸相結合的基本例子嗎？我有一個自變量X，我想回歸到Y但在X上使用AR。任何簡單的例子都會非常有用。

similar question

Answer 1

這是一個快速設立，讓你開始

from statsmodels.tsa.stattools import ARMA 
import pandas as pd 
import numpy as np 

ts = pd.Series(np.random.randn(500), index=pd.date_range('2010-01-01', periods=500)) 

p, q = 1, 1 

arma = ARMA(endog=ts, order=(p, q)).fit() 

print arma.summary() 

           ARMA Model Results        
============================================================================== 
Dep. Variable:      y No. Observations:     500 
Model:      ARMA(1, 1) Log Likelihood    -678.805 
Method:      css-mle S.D. of innovations    0.941 
Date:    Tue, 17 May 2016 AIC       1365.610 
Time:      00:01:52 BIC       1382.469 
Sample:     01-01-2010 HQIC       1372.225 
         - 05-15-2011           
============================================================================== 
       coef std err   z  P>|z|  [95.0% Conf. Int.] 
------------------------------------------------------------------------------ 
const   0.0624  0.048  1.311  0.191  -0.031  0.156 
ar.L1.y  0.3090  0.311  0.992  0.322  -0.302  0.919 
ma.L1.y  -0.2177  0.318  -0.684  0.494  -0.841  0.406 
            Roots          
============================================================================= 
       Real   Imaginary   Modulus   Frequency 
----------------------------------------------------------------------------- 
AR.1   3.2367   +0.0000j   3.2367   0.0000 
MA.1   4.5939   +0.0000j   4.5939   0.0000 
-----------------------------------------------------------------------------