  In this paper, we tackle the problem of prediction and confidence intervals
for time series using a statistical learning approach and quantile loss
functions. In a first time, we show that the Gibbs estimator (also known as
Exponentially Weighted aggregate) is able to predict as well as the best
predictor in a given family for a wide set of loss functions. In particular,
using the quantile loss function of Koenker and Bassett (1978), this allows to
build confidence intervals. We apply these results to the problem of prediction
and confidence regions for the French Gross Domestic Product (GDP) growth, with
promising results.
