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A Generic Method for Density Forecasts Recalibration

  • Jérôme Collet
  • Michael RichardEmail author
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 254)

Abstract

We address the calibration constraint of probability forecasting. We propose a generic method for recalibration, which allows us to enforce this constraint. It remains to be known the impact on forecast quality, measured by predictive distributions sharpness, or specific scores. We show that the impact on the Continuous Ranked Probability Score (CRPS) is weak under some hypotheses and that it is positive under more restrictive ones. We used this method on temperature ensemble forecasts and compared the quality of the recalibrated forecasts with that of the raw ensemble and of a more specific method, that is Ensemble Model Output Statistics (EMOS). Better results are shown with our recalibration rather than with EMOS in this case study.

Keywords

Density forecasting Rosenblatt transform PIT series Calibration Bias correction 

Notes

Acknowledgements

This research was supported by the ANR project FOREWER (ANR-14-CE05-0028).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.EdF R&DPalaiseauFrance
  2. 2.University of OrléansOrléansFrance

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