I’ve been doing some experiments with the idea of training on eras groups and ensemble models.
Taking into account the training dataset has 120 eras (so, 120 sequential months as far as I understood) I join continuous eras in groups of 12 and train them together.
An ensemble model is trained for each group of eras and then performance is evaluated by predicting on the validation data with each of those models. Finally, predictions are averaged.
Each ensemble model is composed by a XGBoostRegressor, a CatBoostRegressor and a LightGBMRegressor.
Models of each ensemble model, are optimized separately using 3 splits kfold cross-validation and no shuffling.
Details are published on a Colab notebook
Any feedback is welcome!