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Damped trend exponential smoothing: A modelling viewpoint

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  • McKenzie, Eddie
  • Gardner Jr., Everette S.
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    Abstract

    Over the past twenty years, damped trend exponential smoothing has performed well in numerous empirical studies, and it is now well established as an accurate forecasting method. The original motivation for this method was intuitively appealing, but said very little about why or when it provided an optimal approach. The aim of this paper is to provide a theoretical rationale for the damped trend method based on Brown's original thinking about the form of underlying models for exponential smoothing. We develop a random coefficient state space model for which damped trend smoothing provides an optimal approach, and within which the damping parameter can be interpreted directly as a measure of the persistence of the linear trend.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 26 (2010)
    Issue (Month): 4 (October)
    Pages: 661-665

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    Handle: RePEc:eee:intfor:v:26:y::i:4:p:661-665

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    Web page: http://www.elsevier.com/locate/ijforecast

    Related research

    Keywords: Time series Exponential smoothing ARIMA models State space models;

    References

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    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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    1. Holt, Charles C., 2004. "Forecasting seasonals and trends by exponentially weighted moving averages," International Journal of Forecasting, Elsevier, vol. 20(1), pages 5-10.
    2. Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
    3. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    4. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    5. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
    6. Everette S. Gardner, Jr. & Ed. Mckenzie, 1985. "Forecasting Trends in Time Series," Management Science, INFORMS, vol. 31(10), pages 1237-1246, October.
    7. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
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    Cited by:
    1. Muslima Zahan & Ron S. Kenett, 2013. "Modeling and Forecasting Energy Consumption in the Manufacturing Industry in South Asia," International Journal of Energy Economics and Policy, Econjournals, vol. 3(1), pages 87-98.
    2. E. Vercher & A. Corberán-Vallet & J. Segura & J. Bermúdez, 2012. "Initial conditions estimation for improving forecast accuracy in exponential smoothing," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 20(2), pages 517-533, July.
    3. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Forecasting aggregate demand: Analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework," International Journal of Production Economics, Elsevier, vol. 146(1), pages 185-198.

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