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In the previous version of the hierarchicalforecast, during the aggregate() , forecasting became balanced.
Used more recent version of the code, and noticed that aggregated dataframe was not balanced.
This can be seen by when grouping by unique_id, number of values are not same for all of them, and also lots of forecasted results are NaN finally, when doing aggregate, then forecast (using statsforecast).
Versions / Dependencies
0.4.0
Reproduction script
Y_df, S_df, tags = aggregate(pd_df, spec)
Issue Severity
None
The text was updated successfully, but these errors were encountered:
When using previous version (0.3.0) , for the same dataframe, I get all time series balanced.
However taking much longer to perform aggregate() then the most recent version.
Hey @iamyihwa, if your series have missing points I suggest you run the fill_gaps function first with start='global', end='global', fill the missing values with zero (which is what the previous version did) and then aggregate.
Yes ! @jmoralez ! It works fine with this method! Thanks for pointing me to it! It would perhaps be helpful to have this also in the documentation of the hierarchicalforecast and other libraries.
What happened + What you expected to happen
In the previous version of the hierarchicalforecast, during the aggregate() , forecasting became balanced.
Used more recent version of the code, and noticed that aggregated dataframe was not balanced.
This can be seen by when grouping by unique_id, number of values are not same for all of them, and also lots of forecasted results are NaN finally, when doing aggregate, then forecast (using statsforecast).
Versions / Dependencies
0.4.0
Reproduction script
Y_df, S_df, tags = aggregate(pd_df, spec)
Issue Severity
None
The text was updated successfully, but these errors were encountered: