Matej08/15/2023, 2:26 PM
For instance, currently the MSTL with AutoArima is not picking up any exogenous regressors. If the prediction window is only the next step clearly the AR MA lags dominate but my objective is e.g. 48 hours ahead and there the covariates might prove beneficial. How can I do this using statsforecast package ? Also is there some summary of fitted model similar to statsmodels? : ) Thanks so much
sf.fit(df = Y_df)
Kevin Kho08/15/2023, 6:04 PM
Mariana Menchero08/15/2023, 9:54 PM
With this, you are fitting a MSTL model using all the information up to the cutoff date. If you want to fit the whole historical data, just change the number of windows to include all possible values. If this is not what you need/want, please elaborate so we can help you with that.
crossvalidation_df = sf.cross_validation( df = df, h = 48, step_size = 48, n_windows = 3 )
Matej08/16/2023, 12:37 PM
for MSTL this only returns the components:
I assume I have to calculate the summary such as likelihood and R^2 by myself out of these numbers or out of
data trend seasonal remainder ...
Sorry for asking dumb questions, some things are not obvious from the documentation.
insample_forecasts = sf.forecast_fitted_values()
Brian Head09/13/2023, 8:48 PM