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Model selection for weakly dependent time series forecasting

dc.contributor.authorAlquier, Pierre
dc.contributor.authorWintenberger, Olivier
dc.date.accessioned2013-09-04T14:33:24Z
dc.date.available2013-09-04T14:33:24Z
dc.date.issued2012
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/11612
dc.language.isoenen
dc.subjectadaptative inferenceen
dc.subjectaggregation of estimatorsen
dc.subjectautoregression estimationen
dc.subjectmodel selectionen
dc.subjectrandomized estimatorsen
dc.subjectstatistical learningen
dc.subjecttime series predictionen
dc.subjectweak dependenceen
dc.subject.ddc519en
dc.titleModel selection for weakly dependent time series forecastingen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenObserving a stationary time series, we propose a two-steps procedure for the prediction of its next value. The first step follows machine learning theory paradigm and consists in determining a set of possible predictors as randomized estimators in (possibly numerous) different predictive models. The second step follows the model selection paradigm and consists in choosing one predictor with good properties among all the predictors of the first step. We study our procedure for two different types of observations: causal Bernoulli shifts and bounded weakly dependent processes. In both cases, we give oracle inequalities: the risk of the chosen predictor is close to the best prediction risk in all predictive models that we consider. We apply our procedure for predictive models as linear predictors, neural networks predictors and nonparametric autoregressive predictors.en
dc.relation.isversionofjnlnameBernoulli
dc.relation.isversionofjnlvol18en
dc.relation.isversionofjnlissue3en
dc.relation.isversionofjnldate2012
dc.relation.isversionofjnlpages883-913en
dc.relation.isversionofdoihttp://dx.doi.org/10.3150/11-BEJ359en
dc.identifier.urlsitehttp://hal.archives-ouvertes.fr/hal-00362151en
dc.relation.isversionofjnlpublisherBernoulli Society for Mathematical Statistics and Probabilityen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen


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