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Robust forecasting with exponential and Holt-Winters smoothing

Author

Listed:
  • 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)

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.

Suggested Citation

  • Sarah Gelper & Roland Fried & Christophe Croux, 2010. "Robust forecasting with exponential and Holt-Winters smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 285-300.
    • Handle: RePEc:jof:jforec:v:29:y:2010:i:3:p:285-300
    DOI: 10.1002/for.1125
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    References listed on IDEAS

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    1. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    2. Anne B. Koehler & Rob J. Hyndman & Ralph D. Snyder & J. Keith Ord, 2005. "Prediction intervals for exponential smoothing using two new classes of state space models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(1), pages 17-37.
    3. Taylor, James W., 2007. "Forecasting daily supermarket sales using exponentially weighted quantile regression," European Journal of Operational Research, Elsevier, vol. 178(1), pages 154-167, April.
    4. T. Cipra & R. Romera, 1997. "Kalman filter with outliers and missing observations," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 6(2), pages 379-395, December.
    5. Everette S. Gardner, 1999. "Note: Rule-Based Forecasting vs. Damped-Trend Exponential Smoothing," Management Science, INFORMS, vol. 45(8), pages 1169-1176, August.
    6. Taylor, James W., 2004. "Volatility forecasting with smooth transition exponential smoothing," International Journal of Forecasting, Elsevier, vol. 20(2), pages 273-286.
    7. James W. Taylor, 2004. "Smooth transition exponential smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 385-404.
    8. Markos Papageorgiou & Apostolos Kotsialos & Antonios Poulimenos, 2005. "Long-term sales forecasting using holt-winters and neural network methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(5), pages 353-368.
    9. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
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