This example is adapted from https://github.com/probabl-ai/forecasting.
The goal is to have a dataset and prediction pipeline that can be used to experiment with features of skore and other packages.
For simplicity, historical data is stored in the repo in datasets. Outputs can
be stored in results (ignored by git). electricity_load_forecasting.py
contains functions for loading data, defining the pipeline, cross-validation
splits etc. . eda.py, cross_validate.py and search.py are scripts that
perform basic exploratory data analysis, cross-validating the default pipeline,
running hyperparameter search & scoring the best model on a held-out set.
requirements.txt lists a few packages used in the scripts.
The current version of the pipeline looks like this:
See the report for an actual run here