Evaluating Probabilistic Forecasts with scoringRules

doi:10.18637/jss.v090.i12

Evaluating Probabilistic Forecasts with scoringRules

2019-01-01

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Alexander I. Jordan *

DOI：10.18637/jss.v090.i12

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Abstract

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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.

Keywords:

comparative evaluation

ensemble forecasts

out-of-sample evaluation

predictive distributions

proper scoring rules

score computation

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Evaluating Probabilistic Forecasts with scoringRules

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