Time series prediction using a recursive algorithm of a combination of genetic programming and constant optimization

Abstract

In this paper we present a prediction process of Time Series using a combination of Genetic Programming and Constant Optimization. The Genetic Programming will be used to evolve the structure of the prediction function, whereas the Constant Optimization will determine the numerical parameters of the prediction function. The prediction process is applied recursively. In each recursion step, a sub-prediction function is evolved. At the end of the iteration all sub-prediction functions form the final prediction function. The avoiding of a major problem in the prediction called over-fitting is also described in this article.nema