A consistent deterministic regression tree for non-parametric prediction   of time series
  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. 
Pierre Gaillard (GREGH), Paul Baudin (INRIA Rocquencourt)
math.ST cs.LG stat.ML stat.TH