# Time Series Prediction Using a Recursive Algorithm of a Combination of Genetic Programming and Constant Optimization

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```@Article{pandyaworayan_2002_MENDELfu,
author =       "Witthaya Panyaworayan and Georg Wuetschner",
title =        "Time Series Prediction Using a Recursive Algorithm of
a Combination of Genetic Programming and Constant
Optimization",
journal =      "Facta Universitatis. Series: Electronics and
Energetics",
year =         "2002",
volume =       "15",
number =       "2",
pages =        "265--279",
month =        aug,
keywords =     "genetic algorithms, genetic programming, Time series
prediction, sunspot numbers",
ISSN =         "0353-3670",
URL =          "http://factaee.elfak.ni.ac.yu/fu2k22/11wp.pdf",
size =         "15 pages",
abstract =     "In this paper we present a prediction process of Time
Series using a combination of Genetic Programming and
Constant Optimisation. 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