How to forecast time series using AutoReg in python

I’m trying to build old school model using only auto regression algorithm. I found out that there’s an implementation of it in statsmodel package. I’ve read the documentation, and as I understand it should work as ARIMA. So, here’s my code:

import statsmodels.api as sm
model = sm.tsa.AutoReg(df_train.beer, 12).fit()

And when I want to predict new values, I’m trying to follow the documentation:

y_pred = model.predict(start=df_test.index.min(), end=df_test.index.max())
# or
y_pred = model.predict(start=100, end=1000)

Both returns a list of NaNs.

Also, when I type model.predict(0, df_train.size - 1) it predicts real values, but model.predict(0, df_train.size) predicts NaNs list.

Am I doing something wrong?

---

P.S. I know there’s ARIMA, ARMA or SARIMAX algorithms, that can be used as basic auto regression. But I need exactly AutoReg.

Answer