Time series prediction by estimating Markov probabilities through topology preserving maps

Gerhard Dangelmayr; Sabino Gadaleta; Douglas Hundley; Michael J. Kirby

doi:10.1117/12.367685

1 November 1999 Time series prediction by estimating Markov probabilities through topology preserving maps

Gerhard Dangelmayr, Sabino Gadaleta, Douglas Hundley, Michael J. Kirby

Proceedings Volume 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II; (1999) https://doi.org/10.1117/12.367685
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

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.

Citation Download Citation

Gerhard Dangelmayr, Sabino Gadaleta, Douglas Hundley, and Michael J. Kirby "Time series prediction by estimating Markov probabilities through topology preserving maps", Proc. SPIE 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, (1 November 1999); https://doi.org/10.1117/12.367685

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