Postprocessing of point predictions for probabilistic forecasting of electricity prices: Diversity matters

arXiv:2404.02270v1 Announce Type: cross
Abstract: Operational decisions relying on predictive distributions of electricity prices can result in significantly higher profits compared to those based solely on point forecasts. However, the majority of models developed in both academic and industrial settings provide only point predictions. To address this, we examine three postprocessing methods for converting point forecasts into probabilistic ones: Quantile Regression Averaging, Conformal Prediction, and the recently introduced Isotonic Distributional Regression. We find that while IDR demonstrates the most varied …

abstract academic arxiv decisions diversity electricity forecasting however industrial predictions predictive profits q-fin.st stat.ap stat.ml type