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Non-homogeneous boosting for predictor selection in ensemble post-processing

Author

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  • Jakob W. Messner
  • Georg J. Mayr
  • Achim Zeileis

Abstract

Non-homogeneous regression is often used to statistically post-process ensemble forecasts. Usually only ensemble forecasts of the predictand variable are used as input but other potentially useful information sources are ignored. Although it is straightforward to add further input variables, overfitting can easily deteriorate the forecast performance for increasing numbers of input variables. This paper proposes a boosting algorithm to estimate the regression coefficients while automatically selecting the most relevant input variables by restricting the coefficients of less important variables to zero. A case study with ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) shows that this approach effectively selects important input variables to clearly improve minimum and maximum temperature predictions at 5 central European stations.

Suggested Citation

  • Jakob W. Messner & Georg J. Mayr & Achim Zeileis, 2016. "Non-homogeneous boosting for predictor selection in ensemble post-processing," Working Papers 2016-04, Faculty of Economics and Statistics, Universität Innsbruck.
  • Handle: RePEc:inn:wpaper:2016-04
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    References listed on IDEAS

    as
    1. Thordis L. Thorarinsdottir & Tilmann Gneiting, 2010. "Probabilistic forecasts of wind speed: ensemble model output statistics by using heteroscedastic censored regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 371-388, April.
    2. Michael Scheuerer & Luca Büermann, 2014. "Spatially adaptive post-processing of ensemble forecasts for temperature," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(3), pages 405-422, April.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    non-homogeneous regression; variable selection; boosting; statistical ensemble post-processing;
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