Arbeitspapier
Non-homogeneous boosting for predictor selection in ensemble post-processing
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Working Papers in Economics and Statistics ; No. 2016-04
- Klassifikation
-
Wirtschaft
- Thema
-
non-homogeneous regression
variable selection
boosting
statistical ensemble postprocessing
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Messner, Jakob W.
Mayr, Georg J.
Zeileis, Achim
- Ereignis
-
Veröffentlichung
- (wer)
-
University of Innsbruck, Research Platform Empirical and Experimental Economics (eeecon)
- (wo)
-
Innsbruck
- (wann)
-
2016
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Messner, Jakob W.
- Mayr, Georg J.
- Zeileis, Achim
- University of Innsbruck, Research Platform Empirical and Experimental Economics (eeecon)
Entstanden
- 2016
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