Published Jun 11, 2013


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Carlos Alberto Martínez, MSc

Juan David Velasquez-Henao, PhD



In this paper 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.


Forecasting, genetic programming, artificial neural networks, genetic algorithms, nonlinear modelsPronóstico, programación genética, redes neuronales artificiales, algoritmos genéticos, modelos no lineales

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How to Cite
Martínez, C. A., & Velasquez-Henao, J. D. (2013). A modification of the methodology of symbolic regression for time series prediction. Ingenieria Y Universidad, 17(2), 325–338.

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