# Sequential Quantile Prediction of Time Series

Abstract : 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
Keywords :
Type de document :
Pré-publication, Document de travail
2009
Domaine :

https://hal.archives-ouvertes.fr/hal-00410120
Contributeur : Benoît Patra <>
Soumis le : mercredi 12 mai 2010 - 13:18:49
Dernière modification le : jeudi 27 avril 2017 - 09:46:28
Document(s) archivé(s) le : jeudi 23 septembre 2010 - 13:08:01

### Fichiers

biaupatra.pdf
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### Identifiants

• HAL Id : hal-00410120, version 2
• ARXIV : 0908.2503

### Citation

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

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