Prediction of chaotic time series using hidden Markov models

This paper describes a methodology of prediction of a chaotic time series as an equivalent stochastic process. It is shown that there is theoretical justification for such a model, and a model is constructed analytically for a known simple chaotic mapping. Possible models for unknown chaotic systems along with methods for estimating their parameters from time series are suggested and their characteristics discussed.