A consistent deterministic regression tree for non-parametric prediction of time series
Statistics Theory
2014-05-12 v2 Machine Learning
Machine Learning
Statistics Theory
Abstract
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.
Cite
@article{arxiv.1405.1533,
title = {A consistent deterministic regression tree for non-parametric prediction of time series},
author = {Pierre Gaillard and Paul Baudin},
journal= {arXiv preprint arXiv:1405.1533},
year = {2014}
}