Model selection for weakly dependent time series forecasting
Alquier, Pierre; Wintenberger, Olivier (2012), Model selection for weakly dependent time series forecasting, Bernoulli, 18, 3, p. 883-913. http://dx.doi.org/10.3150/11-BEJ359
TypeArticle accepté pour publication ou publié
External document linkhttp://hal.archives-ouvertes.fr/hal-00362151
Bernoulli Society for Mathematical Statistics and Probability
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Abstract (EN)Observing 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.
Subjects / Keywordsadaptative inference; aggregation of estimators; autoregression estimation; model selection; randomized estimators; statistical learning; time series prediction; weak dependence
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