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Robust forecasting with exponential and Holt-Winters smoothing

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Author Info

  • Sarah Gelper

    (Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, the Netherlands)

  • Roland Fried

    (Department of Statistics, University of Dortmund, Dortmund, Germany)

  • Christophe Croux

    (Faculty of Business and Economics, Katholieke Universiteit Leuven, Leuven, Belgium)

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    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.

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    File URL: http://hdl.handle.net/10.1002/for.1125
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    Bibliographic Info

    Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

    Volume (Year): 29 (2010)
    Issue (Month): 3 ()
    Pages: 285-300

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    Handle: RePEc:jof:jforec:v:29:y:2010:i:3:p:285-300

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    Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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    Cited by:
    1. Tryggvi Jónsson & Pierre Pinson & Henrik Aa. Nielsen & Henrik Madsen, 2014. "Exponential Smoothing Approaches for Prediction in Real-Time Electricity Markets," Energies, MDPI, Open Access Journal, vol. 7(6), pages 3710-3732, June.
    2. Peter Ruckdeschel & Bernhard Spangl & Daria Pupashenko, 2014. "Robust Kalman tracking and smoothing with propagating and non-propagating outliers," Statistical Papers, Springer, vol. 55(1), pages 93-123, February.
    3. Dicembrino, Claudio & Trovato, Giovanni, 2013. "Structural Breaks, Price and Income Elasticity, and Forecast of the Monthly Italian Electricity Demand," MPRA Paper 47653, University Library of Munich, Germany.
    4. Edward J. LUSK & Michael HALPERIN & Niya STEFANOVA & Atanas TETIKOV, 2011. "Investigation of: "Shopping in the Market-beta Mall"," Journal of Knowledge Management, Economics and Information Technology, ScientificPapers.org, vol. 1(5), pages 9, August.

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