Bayesian forecasting with the Holt–Winters model

J D Bermúdez, J V Segura and E Vercher ()
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J D Bermúdez: Universitat de València
J V Segura: Universidad Miguel Hernández de Elche
E Vercher: Universitat de València

Journal of the Operational Research Society, 2010, vol. 61, issue 1, 164-171

Abstract: Abstract Exponential smoothing methods are widely used as forecasting techniques in inventory systems and business planning, where reliable prediction intervals are also required for a large number of series. This paper describes a Bayesian forecasting approach based on the Holt–Winters model, which allows obtaining accurate prediction intervals. We show how to build them incorporating the uncertainty due to the smoothing unknowns using a linear heteroscedastic model. That linear formulation simplifies obtaining the posterior distribution on the unknowns; a random sample from such posterior, which is not analytical, is provided using an acceptance sampling procedure and a Monte Carlo approach gives the predictive distributions. On the basis of this scheme, point-wise forecasts and prediction intervals are obtained. The accuracy of the proposed Bayesian forecasting approach for building prediction intervals is tested using the 3003 time series from the M3-competition.

Keywords: forecasting; time series; prediction intervals; simulation; M3-competition (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (2)

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DOI: 10.1057/jors.2008.152

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