ARIMAX out of sample forecast with statsmodels












0















Please help me figure out how to do out of sample ARIMA forecast with 1 exogenous variable.



I have a list of list named FILEDATA which contains two lists FILEDATA[0] is the endogenous variable and FILEDATA[1] is he exogenous variable data, they are both the same lenght.



I can do ARIMA forecast easily on FILEDATA[0], but when I add FILEDATA[1] I can't figure out how to insert it in model_fit.forecast().



Read the documentation but it's very ambiguous, I don't understand what type the exog parameter is in the model_fit.forecast() function and in the ARIMA() I am also not sure how elements to supply.



I get errors like IndexError: tuple index out of range, I am not sure what to do.



size        = 4 # min 4
history = list(FILEDATA[0][0:size])
future = FILEDATA[0][size:len(FILEDATA[0])]

history2 = list(FILEDATA[1][0:size])
future2 = FILEDATA[1][size:len(FILEDATA[1])]




begintime = datetime.datetime.now()

predictions = list()
for t in range(len(future)):
model = ARIMA(endog=history, order=(1,1,0), exog=np.array(history2).reshape(len(history2),1) )



model_fit = model.fit(transparams=False, trend='nc',maxiter=20000 , tol=1e-08 , disp=0, method='mle')

actual2 = future2[t]

output = model_fit.forecast(exog=np.array(actual2))

forecast = output[0]
predictions.append(forecast)


actual = future[t]
history.append(actual)



history2.append(actual2)

print(t,'predicted=%f, actual=%f' % (forecast, actual))

endtime = datetime.datetime.now()









share|improve this question



























    0















    Please help me figure out how to do out of sample ARIMA forecast with 1 exogenous variable.



    I have a list of list named FILEDATA which contains two lists FILEDATA[0] is the endogenous variable and FILEDATA[1] is he exogenous variable data, they are both the same lenght.



    I can do ARIMA forecast easily on FILEDATA[0], but when I add FILEDATA[1] I can't figure out how to insert it in model_fit.forecast().



    Read the documentation but it's very ambiguous, I don't understand what type the exog parameter is in the model_fit.forecast() function and in the ARIMA() I am also not sure how elements to supply.



    I get errors like IndexError: tuple index out of range, I am not sure what to do.



    size        = 4 # min 4
    history = list(FILEDATA[0][0:size])
    future = FILEDATA[0][size:len(FILEDATA[0])]

    history2 = list(FILEDATA[1][0:size])
    future2 = FILEDATA[1][size:len(FILEDATA[1])]




    begintime = datetime.datetime.now()

    predictions = list()
    for t in range(len(future)):
    model = ARIMA(endog=history, order=(1,1,0), exog=np.array(history2).reshape(len(history2),1) )



    model_fit = model.fit(transparams=False, trend='nc',maxiter=20000 , tol=1e-08 , disp=0, method='mle')

    actual2 = future2[t]

    output = model_fit.forecast(exog=np.array(actual2))

    forecast = output[0]
    predictions.append(forecast)


    actual = future[t]
    history.append(actual)



    history2.append(actual2)

    print(t,'predicted=%f, actual=%f' % (forecast, actual))

    endtime = datetime.datetime.now()









    share|improve this question

























      0












      0








      0








      Please help me figure out how to do out of sample ARIMA forecast with 1 exogenous variable.



      I have a list of list named FILEDATA which contains two lists FILEDATA[0] is the endogenous variable and FILEDATA[1] is he exogenous variable data, they are both the same lenght.



      I can do ARIMA forecast easily on FILEDATA[0], but when I add FILEDATA[1] I can't figure out how to insert it in model_fit.forecast().



      Read the documentation but it's very ambiguous, I don't understand what type the exog parameter is in the model_fit.forecast() function and in the ARIMA() I am also not sure how elements to supply.



      I get errors like IndexError: tuple index out of range, I am not sure what to do.



      size        = 4 # min 4
      history = list(FILEDATA[0][0:size])
      future = FILEDATA[0][size:len(FILEDATA[0])]

      history2 = list(FILEDATA[1][0:size])
      future2 = FILEDATA[1][size:len(FILEDATA[1])]




      begintime = datetime.datetime.now()

      predictions = list()
      for t in range(len(future)):
      model = ARIMA(endog=history, order=(1,1,0), exog=np.array(history2).reshape(len(history2),1) )



      model_fit = model.fit(transparams=False, trend='nc',maxiter=20000 , tol=1e-08 , disp=0, method='mle')

      actual2 = future2[t]

      output = model_fit.forecast(exog=np.array(actual2))

      forecast = output[0]
      predictions.append(forecast)


      actual = future[t]
      history.append(actual)



      history2.append(actual2)

      print(t,'predicted=%f, actual=%f' % (forecast, actual))

      endtime = datetime.datetime.now()









      share|improve this question














      Please help me figure out how to do out of sample ARIMA forecast with 1 exogenous variable.



      I have a list of list named FILEDATA which contains two lists FILEDATA[0] is the endogenous variable and FILEDATA[1] is he exogenous variable data, they are both the same lenght.



      I can do ARIMA forecast easily on FILEDATA[0], but when I add FILEDATA[1] I can't figure out how to insert it in model_fit.forecast().



      Read the documentation but it's very ambiguous, I don't understand what type the exog parameter is in the model_fit.forecast() function and in the ARIMA() I am also not sure how elements to supply.



      I get errors like IndexError: tuple index out of range, I am not sure what to do.



      size        = 4 # min 4
      history = list(FILEDATA[0][0:size])
      future = FILEDATA[0][size:len(FILEDATA[0])]

      history2 = list(FILEDATA[1][0:size])
      future2 = FILEDATA[1][size:len(FILEDATA[1])]




      begintime = datetime.datetime.now()

      predictions = list()
      for t in range(len(future)):
      model = ARIMA(endog=history, order=(1,1,0), exog=np.array(history2).reshape(len(history2),1) )



      model_fit = model.fit(transparams=False, trend='nc',maxiter=20000 , tol=1e-08 , disp=0, method='mle')

      actual2 = future2[t]

      output = model_fit.forecast(exog=np.array(actual2))

      forecast = output[0]
      predictions.append(forecast)


      actual = future[t]
      history.append(actual)



      history2.append(actual2)

      print(t,'predicted=%f, actual=%f' % (forecast, actual))

      endtime = datetime.datetime.now()






      python-3.x numpy statistics statsmodels arima






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      asked Jan 2 at 5:56









      johnyboy325johnyboy325

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