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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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    5. 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.
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    7. 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.
    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.
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    2. repec:eee:ecosta:v:3:y:2017:i:c:p:91-111 is not listed on IDEAS
    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. Chai, Jian & Zhang, Zhong-Yu & Wang, Shou-Yang & Lai, Kin Keung & Liu, John, 2014. "Aviation fuel demand development in China," Energy Economics, Elsevier, vol. 46(C), pages 224-235.
    5. 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.
    6. 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 1-23, June.
    7. Dorel Paraschiv & Cristiana Tudor & Radu Petrariu, 2015. "The Textile Industry and Sustainable Development: A Holt–Winters Forecasting Investigation for the Eastern European Area," Sustainability, MDPI, Open Access Journal, vol. 7(2), pages 1-12, January.
    8. Francisco Javier Duque-Pintor & Manuel Jesús Fernández-Gómez & Alicia Troncoso & Francisco Martínez-Álvarez, 2016. "A New Methodology Based on Imbalanced Classification for Predicting Outliers in Electricity Demand Time Series," Energies, MDPI, Open Access Journal, vol. 9(9), pages 1-10, September.
    9. 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 1-9, August.

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