A genetic programming approach for time-series analysis and prediction in space physics.

Details A genetic programming approach for time-series analysis and prediction in space physics. A genetic programming approach for time-series analysis and prediction in space physics.

Dec 2004

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004agufmsm41a1121j&link_type=abstract

American Geophysical Union, Fall Meeting 2004, abstract #SM41A-1121

Statistics

Computation

2722 Forecasting, 2784 Solar Wind/Magnetosphere Interactions, 2794 Instruments And Techniques

Scientific paper

A central theme in space weather prediction is the ability to predict time-series of relevant quantities, both empirically, and from physics-based models. Empirical models are often based on educated guesses, or intuition. The task of finding an empirical relationship relating quantities can be tedious and time-consuming, especially when a large number of parameters are involved. Genetic Programming (GP) provides a method for automating the guesswork, and can in some instances automatically find functional relationships between data streams. GP is an evolutionary computation technique which is an extension of the Genetic Algorithm framework used for function optimization. In GP an evolutionary algorithm combines elementary function operators in an attempt to build a function which is able to reproduce a training example from a set of input data. We will illustrate how a GP algorithm can be used in space physics by addressing two relevant topics: The prediction of relativistic electron fluxes, and prediction of Dst.

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