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Abstract: In this paper it is shown how to model times series by using random iterated neural networks with placedependent probabilities. The model assumes that the time series comes from a dynamical system which has a compact global attractor and a physical probability measure supported on the attractor. Also, an evolutionary algorithm is used to train a random iterated neural network that models a financial time series. INTRODUCTION In this paper, we present an extension of random iterated neural... (Update)
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BibTeX entry: (Update)
@misc{ ni-financial,
author = "Fernando Niņo and German Hernandez",
title = "Financial Time Series Modeling with Evolutionary Trained Random Iterated
Neural Networks",
url = "citeseer.ist.psu.edu/437993.html" }
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5 Extracting dynamical behaviour via markov models - Froyland
3 An ergodic theorem for iterated maps. Ergodic Theory and Dyn.. (context) - Elton - 1987
1 Random iterated neural networks: asymptotic behavior (context) - Hernandez, no et al. - 1999
1 Invariant mesures for markov processes arising from iterated.. (context) - Barnsley, Demko et al. - 1988
1 On evolution of stochastic dynamic neural networks (context) - no, Hernandez et al. - 1999
1 Evolutionary design of random iterated neural networks (context) - no, Hernandez et al. - 1999
1 An evolutionary algorithm for fractal coding of binary image.. (context) - Dasgupta, Hernandez et al.
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