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Model selection and randomization for weakly dependent time series forecasting

Wintenberger, Olivier; Alquier, Pierre (2009), Model selection and randomization for weakly dependent time series forecasting, 41èmes Journées de Statistique, SFdS, Bordeaux, 2009, Bordeaux, France, France

Type
Communication / Conférence
External document link
http://hal.inria.fr/inria-00386733/en/
Date
2009
Conference title
41èmes Journées de Statistique, SFdS, Bordeaux
Conference date
2009
Conference city
Bordeaux, France
Conference country
France
Pages
6
Metadata
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Author(s)
Wintenberger, Olivier
Alquier, Pierre
Abstract (EN)
Observing a stationary time series, we propose in this paper new two steps procedures for predicting the next value of the time series. Following machine learning theory paradigm, the first step consists in determining randomized estimators, or "experts", in (possibly numerous) different predictive models. In the second step estimators are obtained by model selection or randomization associated with exponential weights of these experts. We prove Oracle inequalities for both estimators and provide some applications for linear, artificial Neural Networks and additive non-parametric predictors.
Subjects / Keywords
Statistics

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