Nonparametric sequential prediction of time series
Methodology
2008-01-03 v1 Probability
Abstract
Time series prediction covers a vast field of every-day statistical applications in medical, environmental and economic domains. In this paper we develop nonparametric prediction strategies based on the combination of a set of 'experts' and show the universal consistency of these strategies under a minimum of conditions. We perform an in-depth analysis of real-world data sets and show that these nonparametric strategies are more flexible, faster and generally outperform ARMA methods in terms of normalized cumulative prediction error.
Cite
@article{arxiv.0801.0327,
title = {Nonparametric sequential prediction of time series},
author = {Gérard Biau and Kevin Bleakley and László Györfi and György Ottucsák},
journal= {arXiv preprint arXiv:0801.0327},
year = {2008}
}
Comments
article + 2 figures