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

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Papers in Economics and Statistics ; No. 2016-04

Classification and Topics

• Classification: Wirtschaft
• Subject: non-homogeneous regression ; variable selection ; boosting ; statistical ensemble postprocessing

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Messner, Jakob W. ; Mayr, Georg J. ; Zeileis, Achim
• Event: Veröffentlichung
• (who): University of Innsbruck, Research Platform Empirical and Experimental Economics (eeecon)
• (where): Innsbruck
• (when): 2016

Further information

• Handle: http://hdl.handle.net/10419/146121
• Last update: 10.03.2025, 11:45 AM CET