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Financial time series modeling with evolutionary trained random iterated neural networks

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3 Author(s)
F. Nino ; Univ. of Memphis, TN, USA ; F. Hernandez ; A. Parra

The paper shows how to model times series by using random iterated neural networks with place-dependent 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

Published in:

Computational Intelligence for Financial Engineering, 2000. (CIFEr) Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on

Date of Conference:

2000