Forecasting chaotic time series with genetic algorithms

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Abstract
This paper proposes the use of genetic algorithms—search procedures, modeled on the Darwinian theories of natural selection and survival of the fittest—to find equations that describe the behavior of a time series. The method permits global forecasts of such series. Very little data are sufficient to utilize the method and, as a byproduct, these algorithms sometimes indicate the functional form of the dynamic that underlies the data. The algorithms are tested with clean as well as with noisy chaotic data, and with the sunspot series.

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