Robust forecasting with exponential and Holt-Winters smoothing

Sarah Gelper, Roland Fried and Christophe Croux
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Sarah Gelper: Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, the Netherlands, Postal: Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, the Netherlands
Roland Fried: Department of Statistics, University of Dortmund, Dortmund, Germany, Postal: Department of Statistics, University of Dortmund, Dortmund, Germany
Christophe Croux: Faculty of Business and Economics, Katholieke Universiteit Leuven, Leuven, Belgium, Postal: Faculty of Business and Economics, Katholieke Universiteit Leuven, Leuven, Belgium

Journal of Forecasting, 2010, vol. 29, issue 3, 285-300

Abstract: Robust versions of the exponential and Holt-Winters smoothing method for forecasting are presented. They are suitable for forecasting univariate time series in the presence of outliers. The robust exponential and Holt-Winters smoothing methods are presented as recursive updating schemes that apply the standard technique to pre-cleaned data. Both the update equation and the selection of the smoothing parameters are robustified. A simulation study compares the robust and classical forecasts. The presented method is found to have good forecast performance for time series with and without outliers, as well as for fat-tailed time series and under model misspecification. The method is illustrated using real data incorporating trend and seasonal effects. Copyright © 2009 John Wiley & Sons, Ltd.

Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:jof:jforec:v:29:y:2010:i:3:p:285-300

DOI: 10.1002/for.1125

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