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 regre...

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Bibliographic Details
Published in:arXiv.org 2014-05
Main Authors: Gaillard, Pierre, Baudin, Paul
Format: Article
Language:English
Subjects:
Asymptotic properties
Ergodic processes
Regression analysis
Sequences
Online Access:Get full text
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Summary: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.
ISSN:2331-8422