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Forecasting Using Information Theory and Genetic Algorithms
This technology expressed as software is based on the combination of Information Theory and Genetic Algorithms. The InfoEvolve™ Discovery module is used for dimensionality reduction in large data sets, and its Predict module uses a committee of experts approach for accurate prediction or classification. This technology is particularly strong at dimensional reduction (e.g., finding the handful of variables in a 2000 variables set that are important) and in dealing with binary data variables Applicable to any large data set, we present two illustrative example applications: using dimensionality reduction to improve model accuracy for a grain classification instrument, and modeling the movements of the S&P 500 stock index on a day-to-day basis.

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