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Bayesian forecasting with the Holt–Winters model / J. D. Bermúdez in Journal of the operational research society (JORS), Vol. 61 N° 1 (Issue spécial) (Janvier 2010) 

Public ISBD [article]  in Journal of the operational research society (JORS) > Vol. 61 N° 1 (Issue spécial) (Janvier 2010) . - pp. 164–171 Titre : Bayesian forecasting with the Holt–Winters model Type de document : texte imprimé Auteurs : J. D. Bermúdez, Auteur ; J. V. Segura, Auteur ; E. Vercher, Auteur Année de publication : 2011 Article en page(s) : pp. 164–171 Note générale : Recherche opérationnelle Langues : Anglais (eng) Mots-clés : Forecasting  Time  series  Prediction  intervals  Simulation  M3-competition Index. décimale : 001.424 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 [article] Bayesian forecasting with the Holt–Winters model [texte imprimé] / J. D. Bermúdez, Auteur ; J. V. Segura, Auteur ; E. Vercher, Auteur . - 2011 . - pp. 164–171. Recherche opérationnelle Langues : Anglais (eng) in Journal of the operational research society (JORS) > Vol. 61 N° 1 (Issue spécial) (Janvier 2010) . - pp. 164–171 Mots-clés : Forecasting  Time  series  Prediction  intervals  Simulation  M3-competition Index. décimale : 001.424 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