A modification of the methodology of symbolic regression for time series prediction

Created by W.Langdon from gp-bibliography.bib Revision:1.8775

@Article{Martinez:2013:IYU,

• author = "Carlos Alberto Martinez and Juan David Velasquez-Henao",
• title = "A modification of the methodology of symbolic regression for time series prediction",
• journal = "Ingenieria y Universidad",
• year = "2013",
• volume = "17",
• number = "2",
• pages = "325--338",
• month = jul # "-" # dec,
• keywords = "genetic algorithms, genetic programming, Forecasting, artificial neural networks genetic algorithms nonlinear model, LYNX",
• ISSN = "0123-2126",
• URL = "https://revistas.javeriana.edu.co/index.php/iyu/article/view/1736",
• URL = "https://revistas.javeriana.edu.co/index.php/iyu/article/view/1736/5218",
• size = "14 pages",
• abstract = "we propose a new methodology for the prediction of nonlinear time series using genetic programming. The proposed approach is based on incorporating the concept of functional blocks and the modification of the genetic algorithm so that it operates with it. The functional blocks represent well known statistical models for the time series forecasting. The proposed algorithm allows the exploration and exploitation of regions where there is greater possibility of finding better forecasting models. Two Benchmark time series were predicted in order to validate the proposed approach, and it was found that our methodology predicts more accurately the time series considered, in comparison with other nonlinear models.",
• resumen = "En este articulo se propone una nueva metodologia para la prediccin de series de tiempo no lineales usando programacion genetica. La aproximacion propuesta se basa en la incorporacion del concepto de bloques funcionales y la modificacion del algoritmo genetico para que opere con estos. Los bloques funcionales representan modelos estadisticos bien conocidos para el pronostico de series de tiempo. El algoritmo propuesto permite la exploracion y explotacion de regiones donde hay mayor posibilidad de encontrar mejores modelos de pronostico. Para validar la aproximacion propuesta, se pronosticaron dos series de tiempo Benchmark; se encontro que nuestra metodologia pronostica con mayor precision las series de tiempo consideradas en comparacion con otros modelos no lineales.",
• notes = "In spanish

  Universidad Nacional de Colombia",

}

Genetic Programming entries for Carlos Alberto Martinez Juan David Velasquez Henao

Citations