| Titre : | Bayesian forecasting with the Holt–Winters model (2011) |
| Auteurs : | J. D. Bermúdez, Auteur ; J. V. Segura, Auteur ; E. Vercher, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | Journal of the operational research society (JORS) (Vol. 61 N° 1 (Issue spécial), Janvier 2010) |
| Article en page(s) : | pp. 164–171 |
| Note générale : | Recherche opérationnelle |
| Langues : | Anglais |
| Index. décimale : | 001.424 |
| Tags : | Forecasting Time series Prediction intervals Simulation M3-competition |
| Résumé : | 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. |
| DEWEY : | 001.424 |
| ISSN : | 0160-5682 |
| En ligne : | http://www.palgrave-journals.com/jors/journal/v61/n1/abs/jors2008152a.html |
