Time series prediction with neural networks: a case study of two examples

A new approach to optimal presentation of a real world problem to a neural network that predicts chaotic time series is presented in the following paper. The proposed approach takes advantage of two measures of deterministic chaos in time series-the correlation exponent and the mutual information function. Its successfulness is most promising and is demonstrated on two time series.<
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