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Article Dans Une Revue IEEE Transactions on Information Theory Année : 2011

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.

Dates et versions

hal-00606486 , version 1 (06-07-2011)

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Citer

Gérard Biau, B. Patra. Sequential quantile prediction of time series. IEEE Transactions on Information Theory, 2011, 57 (3), pp.1664-1674. ⟨10.1109/TIT.2011.2104610⟩. ⟨hal-00606486⟩
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