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
Type de document :
Pré-publication, Document de travail
2009
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-00410120
Contributeur : Benoît Patra <>
Soumis le : mercredi 12 mai 2010 - 13:18:49
Dernière modification le : mercredi 12 octobre 2016 - 01:04:16
Document(s) archivé(s) le : jeudi 23 septembre 2010 - 13:08:01

Fichiers

biaupatra.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

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

Collections

Citation

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

Partager

Métriques

Consultations de
la notice

151

Téléchargements du document

111