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- W4225502034 abstract "Distributional forecasts are important for a wide variety of applications, including forecasting epidemics. Often, forecasts are miscalibrated, or unreliable in assigning uncertainty to future events. We present a recalibration method that can be applied to a black-box forecaster given retrospective forecasts and observations, as well as an extension to make this method more effective in recalibrating epidemic forecasts. This method is guaranteed to improve calibration and log score performance when trained and measured in-sample. We also prove that the increase in expected log score of a recalibrated forecaster is equal to the entropy of the PIT distribution. We apply this recalibration method to the 27 influenza forecasters in the FluSight Network and show that recalibration reliably improves forecast accuracy and calibration. This method is effective, robust, and easy to use as a post-processing tool to improve epidemic forecasts." @default.
- W4225502034 created "2022-05-05" @default.
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- W4225502034 date "2021-12-12" @default.
- W4225502034 modified "2023-09-23" @default.
- W4225502034 title "Recalibrating probabilistic forecasts of epidemics" @default.
- W4225502034 doi "https://doi.org/10.48550/arxiv.2112.06305" @default.
- W4225502034 hasPublicationYear "2021" @default.
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