  Observing a stationary time series, we propose a two-step procedure for the
prediction of the next value of the time series. The first step follows machine
learning theory paradigm and consists in determining a set of possible
predictors as randomized estimators in (possibly numerous) different predictive
models. The second step follows the model selection paradigm and consists in
choosing one predictor with good properties among all the predictors of the
first steps. We study our procedure for two different types of bservations:
causal Bernoulli shifts and bounded weakly dependent processes. In both cases,
we give oracle inequalities: the risk of the chosen predictor is close to the
best prediction risk in all predictive models that we consider. We apply our
procedure for predictive models such as linear predictors, neural networks
predictors and non-parametric autoregressive.
