Sequential Quantile Prediction of Time Series

13 years 16 days ago
Sequential Quantile Prediction of Time Series
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.
Gérard Biau, Benoît Patra
Added 15 May 2011
Updated 15 May 2011
Type Journal
Year 2011
Where TIT
Authors Gérard Biau, Benoît Patra
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