Non-homogeneous boosting for predictor selection in ensemble post-processing
Jakob W. Messner (),
Georg J. Mayr () and
Achim Zeileis ()
Working Papers from Faculty of Economics and Statistics, University of Innsbruck
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.
Keywords: non-homogeneous regression; variable selection; boosting; statistical ensemble post-processing (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:inn:wpaper:2016-04
Access Statistics for this paper
More papers in Working Papers from Faculty of Economics and Statistics, University of Innsbruck Contact information at EDIRC.
Series data maintained by Janette Walde ().