(temporian developer here) Here's a simplified version of the approach (i.e. performing strong feature engineering, then converting the multivariate time serie

(temporian developer here) Here's a simplified version of the approach (i.e. performing strong feature engineering, then converting the multivariate time series data to a panel/tabular dataset and training a boosting trees model on it), using temporian (a much improved alternative to pandas for working with temporal data) and xgboost: https://temporian.readthedocs.io/en/stable/tutorials/m5_competition/ https://temporian.readthedocs.io/en/stable/tutorials/m5_comp...