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Pré-Publication, Document De Travail Année : 2009

Sequential Quantile Prediction of Time Series

Résumé

Motivated by a broad range of potential applications, we address the quantile prediction problem of real-valued time series. We present a sequential quantile forecasting model based on the combination of a set of elementary nearest neighbor-type predictors called ``experts'' and show its consistency under a minimum of conditions. Our approach builds on the methodology developed in recent years for prediction of individual sequences and exploits the quantile structure as a minimizer of the so-called pinball loss function. We perform an in-depth analysis of real-world data sets and show that this nonparametric strategy generally outperforms standard quantile prediction methods
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Dates et versions

hal-00410120 , version 1 (17-08-2009)
hal-00410120 , version 2 (12-05-2010)

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Gérard Biau, Benoît Patra. Sequential Quantile Prediction of Time Series. 2009. ⟨hal-00410120v2⟩
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