文章基本信息

标题：Evaluating Probabilistic Forecasts with scoringRules
本地全文：下载
作者：Alexander Jordan ; Fabian Krüger ; Sebastian Lerch 等
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2019
卷号：90
期号：1
页码：1-37
DOI：10.18637/jss.v090.i12
出版社：University of California, Los Angeles
摘要：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.
关键词：comparative evaluation; ensemble forecasts; out-of-sample evaluation; predictive distributions; proper scoring rules; score computation; R.
其他关键词：comparative evaluation;ensemble forecasts;out-of-sample evaluation;predictive distributions;proper scoring rules;score computation;R