A new Bayesian formulation for Holt's exponential smoothing

Robert R. Andrawis and Amir Atiya ()
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Robert R. Andrawis: Data Mining Center of Excellence, MCIT, Cairo, Egypt, Postal: Data Mining Center of Excellence, MCIT, Cairo, Egypt

Journal of Forecasting, 2009, vol. 28, issue 3, pages 218-234

Abstract: 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.

Date: 2009

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Persistent link: http://EconPapers.repec.org/RePEc:jof:jforec:v:28:y:2009:i:3:p:218-234

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