BellmanPredict

Hurdle Model Combination

Throughout the project, binary classification probabilities and regression forecasts are combined using probability multiplication. The final demand estimate for each horizon is computed as p * \hat{y}, where p is the predicted probability of non-zero demand. This convention is implemented in LGBMTrainer, the standalone LightGBM utilities, and the Optuna tuning objective.

Preprocessing

SampleWindowizer.build_lgbm_train now vectorizes target generation and unpivoting, producing the same dataset as the previous row-wise implementation but more efficiently.

Baseline Forecasting

Run baseline models with the provided configuration:

python LGHackerton/train_baseline.py --config configs/baseline.yaml --model naive

PatchTST Grid Search

Run a grid search over PatchTST settings:

python LGHackerton/tune.py --task patchtst_grid --config configs/patchtst.yaml

PatchTST Hyperparameter Selection

train.py determines PatchTST settings using the following order:

1. If artifacts/patchtst_search.csv exists (created by the grid-search task), the combination with the lowest val_wsmape is chosen. The associated input_len is applied to window generation and to the model.
2. Otherwise, Optuna results from artifacts/optuna/patchtst_best.json are used when available.
3. If neither artifact is present, default parameters from PATCH_PARAMS are used.

This avoids confusion when both grid-search and Optuna artifacts may exist.