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A state space model for exponential smoothing with group seasonality

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  • Pim Ouwehand
  • Rob J. Hyndman

    ()

  • Ton G. de Kok
  • Karel H. van Donselaar

Abstract

We present an approach to improve forecast accuracy by simultaneously forecasting a group of products that exhibit similar seasonal demand patterns. Better seasonality estimates can be made by using information on all products in a group, and using these improved estimates when forecasting at the individual product level. This approach is called the group seasonal indices (GSI) approach, and is a generalization of the classical Holt-Winters procedure. This article describes an underlying state space model for this method and presents simulation results that show when it yields more accurate forecasts than Holt-Winters.

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File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2007/wp7-07.pdf
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Bibliographic Info

Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 7/07.

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Length: 26 pages
Date of creation: Jun 2007
Date of revision:
Handle: RePEc:msh:ebswps:2007-7

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Keywords: Common seasonality; demand forecasting; exponential smoothing; Holt-Winters; state space model.;

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References

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  1. 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.
  2. Koehler, Anne B. & Snyder, Ralph D. & Ord, J. Keith, 2001. "Forecasting models and prediction intervals for the multiplicative Holt-Winters method," International Journal of Forecasting, Elsevier, vol. 17(2), pages 269-286.
  3. Rob J. Hyndman & Anne B. Koehler, 2005. "Another Look at Measures of Forecast Accuracy," Monash Econometrics and Business Statistics Working Papers 13/05, Monash University, Department of Econometrics and Business Statistics.
  4. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
  5. Bunn, Derek W. & Vassilopoulos, A. I., 1993. "Using group seasonal indices in multi-item short-term forecasting," International Journal of Forecasting, Elsevier, vol. 9(4), pages 517-526, December.
  6. Dekker, Mark & van Donselaar, Karel & Ouwehand, Pim, 2004. "How to use aggregation and combined forecasting to improve seasonal demand forecasts," International Journal of Production Economics, Elsevier, vol. 90(2), pages 151-167, July.
  7. Withycombe, Richard, 1989. "Forecasting with combined seasonal indices," International Journal of Forecasting, Elsevier, vol. 5(4), pages 547-552.
  8. Archibald, Blyth C. & Koehler, Anne B., 2003. "Normalization of seasonal factors in Winters' methods," International Journal of Forecasting, Elsevier, vol. 19(1), pages 143-148.
  9. 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.
  10. Bunn, Derek W. & Vassilopoulos, Angelos I., 1999. "Comparison of seasonal estimation methods in multi-item short-term forecasting," International Journal of Forecasting, Elsevier, vol. 15(4), pages 431-443, October.
  11. Ord, J.K. & Koehler, A. & Snyder, R.D., 1995. "Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models," Monash Econometrics and Business Statistics Working Papers 4/95, Monash University, Department of Econometrics and Business Statistics.
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