Still in the process of understanding the ARIMA source code to forecast some data. (I use two time series (indexed_df and external_df with 365 data points each.)
I want to compare the forecast accuracy between ARMA and ARMAX.
The forecasting process for ARMA seems to work fine. But forecasting with one additional external variable does not work somehow:
Getting p and q values for ARMAX:
arma_mod1 = sm.tsa.ARMA(indexed_df, (2,0), external_df).fit()
y = arma_mod1.params
print 'P- and Q-Values(ARMAX):'
print y
Out:
P- and Q-Values(ARMAX):
const 34.739272
0 0.000136
ar.L1.0 0.578090
ar.L2.0 0.129253
dtype: float64
Getting an predicted value (in-sample):
start_pred = '2013-12-30'
end_pred = '2013-12-30'
period = (start_pred, end_pred)
predict_price1 = arma_mod1.predict(start_pred, end_pred, exog=True, dynamic=True)
print ('Predicted Price (ARMAX): {}' .format(predict_price1))
Out:
Traceback (most recent call last):
File "<ipython-input-102-78b3d705d411>", line 6, in <module>
predict_price1 = arma_mod1.predict(start_pred, end_pred, exog=True, dynamic=True)
File "/Applications/anaconda/lib/python2.7/site-packages/statsmodels-0.6.1-py2.7-macosx-10.5-x86_64.egg/statsmodels/base/wrapper.py", line 92, in wrapper
return data.wrap_output(func(results, *args, **kwargs), how)
File "/Applications/anaconda/lib/python2.7/site-packages/statsmodels-0.6.1-py2.7-macosx-10.5-x86_64.egg/statsmodels/tsa/arima_model.py", line 1441, in predict
return self.model.predict(self.params, start, end, exog, dynamic)
File "/Applications/anaconda/lib/python2.7/site-packages/statsmodels-0.6.1-py2.7-macosx-10.5-x86_64.egg/statsmodels/tsa/arima_model.py", line 736, in predict
start, method)
File "/Applications/anaconda/lib/python2.7/site-packages/statsmodels-0.6.1-py2.7-macosx-10.5-x86_64.egg/statsmodels/tsa/arima_model.py", line 327, in _arma_predict_out_of_sample
exog)
File "/Applications/anaconda/lib/python2.7/site-packages/statsmodels-0.6.1-py2.7-macosx-10.5-x86_64.egg/statsmodels/tsa/arima_model.py", line 293, in _get_predict_out_of_sample
X = lagmat(np.dot(exog, exparams), p, original='in', trim='both')
File "/Applications/anaconda/lib/python2.7/site-packages/statsmodels-0.6.1-py2.7-macosx-10.5-x86_64.egg/statsmodels/tsa/tsatools.py", line 328, in lagmat
raise ValueError("maxlag should be < nobs")
ValueError: maxlag should be < nobs
My understanding of maxlag is that (if not defined before) the number of lags to be observed will be automatically calculated with:
maxlag = int(round(12*(nobs/100.)**(1/4.)
but i dont understand where i might change this calculation or set the number of maxlag.
My understanding of nobs is the number of time steps, i.e. values i have in my time series. (in my case 365).
So that means i need maxlag < 365, right?
Where can i define the number of maxlag?
The same error occurred in this question: ADF test in statsmodels in Python but i have no clue where to set maxlag for ARMAX prediction.
Help appreciated
The code:
predict_price1 = arma_mod1.predict(start_pred, end_pred, exog=True, dynamic=True)
print ('Predicted Price (ARMAX): {}' .format(predict_price1))
has to be changed into:
predict_price1 = arma_mod1.predict(start_pred, end_pred, external_df, dynamic=True)
print ('Predicted Price (ARMAX): {}' .format(predict_price1))
that way it works!
I compared the values without external_dfand they where different which can be seen as a proof i guess.