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Nonparametric sequential prediction of time series
- DOI:
- 10.1080/10485250802680730
pages 297-317
Available online: 06 Aug 2009Abstract
Time series prediction covers a vast field of everyday 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 normalised cumulative prediction error.
Keywords
- time series,
- sequential prediction,
- universal consistency,
- kernel estimation,
- nearest neighbour estimation,
- generalised linear estimates
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- Available online: 06 Aug 2009
Author affiliations
- a LSTA & LPMA, Université Pierre et Marie Curie, Paris VI, Paris, France
- b Institut Curie, Centre de Recherche, Paris, France
- c INSERM, U900, Paris, France
- d Centre for Computational Biology, Ecole des Mines de Paris, Fontainebleau, France
- e Department of Computer Science and Information Theory, Budapest University of Technology and Economics, Budapest, Hungary
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