  We study online prediction of bounded stationary ergodic processes. To do so,
we consider the setting of prediction of individual sequences and build a
deterministic regression tree that performs asymptotically as well as the best
L-Lipschitz constant predictors. Then, we show why the obtained regret bound
entails the asymptotical optimality with respect to the class of bounded
stationary ergodic processes.
