Forecasting chaotic time series with genetic algorithms

Details Forecasting chaotic time series with genetic algorithms Forecasting chaotic time series with genetic algorithms

Mar 1997

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1997phrve..55.2557s&link_type=abstract

Physical Review E (Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics), Volume 55, Issue 3, March 1997,

Physics

22

Scientific paper

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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