Bayesian forecasting with the Holt–Winters model

Bayesian forecasting with the Holt–Winters model

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Article ID: iaor200973023
Country: United Kingdom
Volume: 61
Issue: 1
Start Page Number: 164
End Page Number: 171
Publication Date: Jan 2010
Journal: Journal of the Operational Research Society
Authors: , ,
Keywords: Bayesian forecasting
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.

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