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Smooth transition exponential smoothing

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  • James W. Taylor

    (Saïd Business School, University of Oxford, UK)

Abstract

Adaptive exponential smoothing methods allow a smoothing parameter to change over time, in order to adapt to changes in the characteristics of the time series. However, these methods have tended to produce unstable forecasts and have performed poorly in empirical studies. This paper presents a new adaptive method, which enables a smoothing parameter to be modelled as a logistic function of a user-specified variable. The approach is analogous to that used to model the time-varying parameter in smooth transition models. Using simulated data, we show that the new approach has the potential to outperform existing adaptive methods and constant parameter methods when the estimation and evaluation samples both contain a level shift or both contain an outlier. An empirical study, using the monthly time series from the M3-Competition, gave encouraging results for the new approach. Copyright © 2004 John Wiley & Sons, Ltd.

Suggested Citation

  • James W. Taylor, 2004. "Smooth transition exponential smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 385-404.
  • Handle: RePEc:jof:jforec:v:23:y:2004:i:6:p:385-404
    DOI: 10.1002/for.918
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    References listed on IDEAS

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    1. Taylor, James W., 2004. "Volatility forecasting with smooth transition exponential smoothing," International Journal of Forecasting, Elsevier, vol. 20(2), pages 273-286.
    2. Williams, Dan W. & Miller, Don, 1999. "Level-adjusted exponential smoothing for modeling planned discontinuities1," International Journal of Forecasting, Elsevier, vol. 15(3), pages 273-289, July.
    3. Hyndman, R.J. & Koehler, A.B. & Ord, J.K. & Snyder, R.D., 2001. "Prediction Intervals for Exponential Smoothing State Space Models," Monash Econometrics and Business Statistics Working Papers 11/01, Monash University, Department of Econometrics and Business Statistics.
    4. Bunn, DW, 1981. "Adaptive forecasting using the Kalman filter," Omega, Elsevier, vol. 9(3), pages 323-324.
    5. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    6. Taylor, James W., 2003. "Exponential smoothing with a damped multiplicative trend," International Journal of Forecasting, Elsevier, vol. 19(4), pages 715-725.
    7. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737.
    8. Phillip G. Enns & Joseph A. Machak & W. Allen Spivey & William J. Wrobleski, 1982. "Forecasting Applications of an Adaptive Multiple Exponential Smoothing Model," Management Science, INFORMS, vol. 28(9), pages 1035-1044, September.
    9. Adya, Monica & Collopy, Fred & Armstrong, J. Scott & Kennedy, Miles, 2001. "Automatic identification of time series features for rule-based forecasting," International Journal of Forecasting, Elsevier, vol. 17(2), pages 143-157.
    10. Ekern, Steinar, 1982. "On simulation studies of adaptive forecasts," Omega, Elsevier, vol. 10(1), pages 91-93.
    11. Pantazopoulos, Sotiris N. & Pappis, Costas P., 1996. "A new adaptive method for extrapolative forecasting algorithms," European Journal of Operational Research, Elsevier, vol. 94(1), pages 106-111, October.
    12. James W. Taylor & Derek W. Bunn, 1999. "A Quantile Regression Approach to Generating Prediction Intervals," Management Science, INFORMS, vol. 45(2), pages 225-237, February.
    13. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    14. Balke, Nathan S, 1993. "Detecting Level Shifts in Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 81-92, January.
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    Cited by:

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    2. Liu, Min & Taylor, James W. & Choo, Wei-Chong, 2020. "Further empirical evidence on the forecasting of volatility with smooth transition exponential smoothing," Economic Modelling, Elsevier, vol. 93(C), pages 651-659.
    3. 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.
    4. Neil Shephard, 2013. "Martingale unobserved component models," Economics Papers 2013-W01, Economics Group, Nuffield College, University of Oxford.
    5. Koehler, Anne B. & Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "A study of outliers in the exponential smoothing approach to forecasting," International Journal of Forecasting, Elsevier, vol. 28(2), pages 477-484.
    6. 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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