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A new Bayesian formulation for Holt's exponential smoothing

Listed author(s):
  • Robert R. Andrawis

    (Data Mining Center of Excellence, MCIT, Cairo, Egypt)

  • Amir F. Atiya

    (Department of Computer Engineering, Cairo University, Giza, Egypt)

In this paper we propose a Bayesian forecasting approach for Holt's additive exponential smoothing method. Starting from the state space formulation, a formula for the forecast is derived and reduced to a two-dimensional integration that can be computed numerically in a straightforward way. In contrast to much of the work for exponential smoothing, this method produces the forecast density and, in addition, it considers the initial level and initial trend as part of the parameters to be evaluated. Another contribution of this paper is that we have derived a way to reduce the computation of the maximum likelihood parameter estimation procedure to that of evaluating a two-dimensional grid, rather than applying a five-variable optimization procedure. Simulation experiments confirm that both proposed methods give favorable performance compared to other approaches. Copyright © 2008 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.1094
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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 28 (2009)
Issue (Month): 3 ()
Pages: 218-234

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Handle: RePEc:jof:jforec:v:28:y:2009:i:3:p:218-234
DOI: 10.1002/for.1094
Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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  1. 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.
  2. Makridakis, Spyros & Hibon, Michele, 1991. "Exponential smoothing: The effect of initial values and loss functions on post-sample forecasting accuracy," International Journal of Forecasting, Elsevier, vol. 7(3), pages 317-330, November.
  3. Laurence Broze & Guy Melard, 1990. "Exponential smoothing: estimation by maximum likelihood," ULB Institutional Repository 2013/13716, ULB -- Universite Libre de Bruxelles.
  4. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
  5. Forbes, C.S. & Snyder, R.D. & Shami, R.S., 2000. "Bayesian Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 7/00, Monash University, Department of Econometrics and Business Statistics.
  6. Koning, Alex J. & Franses, Philip Hans & Hibon, Michele & Stekler, H.O., 2005. "The M3 competition: Statistical tests of the results," International Journal of Forecasting, Elsevier, vol. 21(3), pages 397-409.
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