Lag generation process optimization

[antoinecarme]

PyAF generates a lot of lags for each cycle residue to compute additional signal components (AR, ARX, SVR, XGB, XGBX, ...)

These lags are generated on the same CPU for each cycle residue to compute a whole set of models.

The generated lags can be shared between all these models, using the same lags internal dataframe. Keras, XGBoost and Scikit-Learn models can use the same input numpy vectors.

This is a CPU time + memory optimization. No impact on forecast models and/or forecast values is expected.

Release date : 2022-07-14

[antoinecarme]

Ensure that PyAF is and remains Green #176

[antoinecarme]

This issue is linked with #190. Generating too many lags and merging the resulting dataframes leads to some re-indexing issues.

[antoinecarme]

FIXED.