Evaluating Probabilistic Forecasters with sktime and tsbootstrap — Easy-to-Use, Configurable Frameworks for Reproducible Science
Benedikt Heidrich, Sankalp Gilda, Franz J. Király · Proceedings of the Python in Science Conferences · 2024
Evaluating probabilistic forecasts is complex and essential across various domains, yet no comprehensive software framework exists to simplify this task. Despite extensive literature on evaluation methodologies, current practices are fragmented and often lack reproducibility. To address this gap, we introduce a reproducible experimental workflow for evaluating probabilistic forecasting algorithms using the sktime package. Our framework features a unified software API for forecasting algorithms, a simple specification language for complex algorithms, including meta-algorithms like bootstrapping, probabilistic performance metrics, and standardized evaluation workflows. We demonstrate the framework’s efficacy through a study evaluating prediction intervals added to point forecasts. Our results highlight the improved prediction accuracy and reliability of combined approaches. We provide reusable code and invite contributions from the research community to extend our experiments and tackle computational challenges for broader studies.