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Proceedings Paper

Time series prediction by estimating Markov probabilities through topology preserving maps
Author(s): Gerhard Dangelmayr; Sabino Gadaleta; Douglas Hundley; Michael J. Kirby
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Paper Abstract

Topology preserving maps derived from neural network learning algorithms are well suited to approximate probability distributions from data sets. We use such algorithms to generate maps which allow the prediction of future events from a sample time series. Our approach relies on computing transition probabilities modeling the time series as a Markov process. Thus the technique can be applied both to stochastic as well as to deterministic chaotic data and also permits the computation of `error bars' for estimating the quality of predictions. We apply the method to the prediction of measured chaotic and noisy time series.

Paper Details

Date Published: 1 November 1999
PDF: 8 pages
Proc. SPIE 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, (1 November 1999); doi: 10.1117/12.367685
Show Author Affiliations
Gerhard Dangelmayr, Colorado State Univ. (United States)
Sabino Gadaleta, Colorado State Univ. (United States)
Douglas Hundley, Whitman College (United States)
Michael J. Kirby, Colorado State Univ. (United States)


Published in SPIE Proceedings Vol. 3812:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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