Evaluating probabilistic forecasts with scoringRules

Abstract

Probabilistic forecasts in the form of probability distributions over future events have become popular in several fields including meteorology, hydrology, economics, and demography. In typical applications, many alternative statistical models and data sources can be used to produce probabilistic forecasts. Hence, evaluating and selecting among competing methods is an important task. The scoringRules package for R provides functionality for comparative evaluation of probabilistic models based on proper scoring rules, covering a wide range of situations in applied work. This paper discusses implementation and usage details, presents case studies from meteorology and economics, and points to the relevant background literature.

References in corpus (1)

• Probabilistic wind speed forecasting on a grid based on ensemble model output statistics

Cited by in corpus (4)

• Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison
• Probabilistic Temperature Forecasting with a Heteroscedastic Autoregressive Ensemble Postprocessing model
• Postprocessing of Ensemble Weather Forecasts Using Permutation-invariant Neural Networks
• Improving probabilistic forecasts of extreme wind speeds by training statistical post-processing models with weighted scoring rules