Model selection for weakly dependent time series forecasting

Pierre Alquier (1, 2) , Olivier Wintenberger (3)

1 LPMA - Laboratoire de Probabilités et Modèles Aléatoires
2 CREST - Centre de Recherche en Économie et Statistique
3 CEREMADE - CEntre de REcherches en MAthématiques de la DEcision

Observing a stationary time series, we propose a two-step procedure for the prediction of the next value of the time series. 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 steps. We study our procedure for two different types of bservations: 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 such as linear predictors, neural networks predictors and non-parametric autoregressive.

Mots clés: Time series prediction; autoregression estimation; adaptative inference; statistical learning; randomized estimators; aggregation of estimators; model selection; weak dependence

Domaines: Méthodologie [stat.ME]; Statistiques [math.ST]; Théorie [stat.TH]

Pierre Alquier, Olivier Wintenberger. Model selection for weakly dependent time series forecasting. Bernoulli, 2012, 18 (3), pp.883-913. ⟨10.3150/11-BEJ359⟩. ⟨hal-00362151v4⟩