ARIMA ( statsmodels.tsa.arima_model.ARIMA
), AR ( statsmodels.tsa.ar_model.AR
), and ARMA ( statsmodels.tsa.arima_model.ARMA
) in statsmodels all take in the parameters of their model in their predict
method. For example, for the AR object, we have the following function definitions:
AR(endog, dates=None, freq=None, missing='none')[source]
fit([maxlag, method, ic, trend, ...])
predict(params[, start, end, dynamic])
( Link to documentation here )
I'm actually very confused about the parameter choices for predict
. predict
's first parameter is the parameters to the constructor of AR
; it doesn't make sense that these once again appear in the parameter for predict
. They also appear for the constructors for ARIMA
and ARMA
. Can someone answer why this parameter exists?
For what its worth, I don't have much background in time series analysis, so perhaps there is some functionality that is exposed when reusing parameters. Otherwise, this parameter is a nuisance.
I answered your question on the issue tracker here . You want to call predict on the results object returned from fit. This the pattern that we follow.
model = sm.tsa.ARMA(y, (2, 2))
results = model.fit()
results.predict()
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