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: | Bermdez J D, Segura J V, Vercher E |
Keywords: | Bayesian forecasting |
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.