  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
