Open AccessTime series prediction using a recursive algorithm of a combination of genetic programming and constant optimization
Author(s) -
Witthaya Panyaworayan,
Georg Wuetschner
Publication year - 2002
Publication title -
facta universitatis. series electronics and energetics/facta universitatis. series: electronics and energetics
Language(s) - English
Resource type - Journals
eISSN - 2217-5997
pISSN - 0353-3670
DOI - 10.2298/fuee0202265p
Subject(s) - recursion (computer science) , genetic programming , series (stratigraphy) , constant (computer programming) , algorithm , computer science , function (biology) , mathematical optimization , test functions for optimization , time series , genetic algorithm , process (computing) , mathematics , optimization problem , artificial intelligence , machine learning , multi swarm optimization , paleontology , operating system , evolutionary biology , biology , programming language
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.