PostForecasts.jl: A Julia package for probabilistic forecasting by postprocessing point predictions

Arkadiusz Lipiecki and Rafał Weron

No WORMS/25/02, WORking papers in Management Science (WORMS) from Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology

Abstract: Postprocessing of point predictions is a relatively simple and efficient way to compute probabilistic forecasts, which are the basis of uncertainty assessment for decision support and risk management. The PostForecasts.jl package in Julia provides types and functions to easily convert point forecasts into probabilistic ones using Historical Simulation, Conformal Prediction, Isotonic Distributional Regression, and variants of Quantile Regression Averaging. By leveraging the developments in the point forecasting literature, it offers a set of easy-to-use, computationally undemanding, and robust tools to derive predictive distributions.

Keywords: Probabilistic forecasting; Postprocessing; Combining forecasts; Uncertainty quantification (search for similar items in EconPapers)
JEL-codes: C22 C51 C53 Q41 Q47 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2025
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https://worms.pwr.edu.pl/RePEc/ahh/wpaper/WORMS_25_02.pdf Original version, 28.02.2025 (application/pdf)

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