.png){kind=icon}
返回
Model Diagnostics and Forecast Evaluation for Quantiles
.png){kind=icon}
OA
AI
DOI：10.1146/annurev-statistics-032921-020240.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Model diagnostics and forecast evaluation are closely related tasks, with the former concerning in-sample goodness (or lack) of fit and the latter addressing predictive performance out-of-sample. We review the ubiquitous setting in which forecasts are cast in the form of quantiles or quantile-bounded prediction intervals. We distinguish unconditional calibration, which corresponds to classical coverage criteria, from the stronger notion of conditional calibration, as can be visualized in quantile reliability diagrams. Consistent scoring functions-including, but not limited to, the widely used asymmetric piecewise linear score or pinball loss-provide for comparative assessment and ranking, and link to the coefficient of determination and skill scores.We illustrate the use of these tools on Engel's food expenditure data, the Global Energy Forecasting Competition 2014, and the US COVID-19 Forecast Hub.
Keyword:
calibration
coverage plot
Murphy diagram
quantile regression
reliability diagram
scoring function
期刊
IF:
8.7
论文数:
211
被引数:
2.4K
机构
引用论文
A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave.png)
NATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏Forecaster's Dilemma: Extreme Events and Forecast Evaluation预报员的困境: 极端事件和预测评估STATISTICAL SCIENCEOAAI
STATISTICAL SCIENCEIF3.4
OAAI分享收藏
查看更多内容