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Building ARMA Models with Genetic Algorithms

  • Tommaso Minerva
  • Irene Poli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2037)

Abstract

The current state of the art in selecting ARMA time series models requires competence and experience on the part of the practitioner, and sometimes the results are not very satisfactory. In this paper, we propose a new automatic approach to the model selection problem, based upon evolutionary computation. We build a genetic algorithm which evolves the representation of a predictive model, choosing both the orders and the predictors of the model. In simulation studies, the procedure succeeded in identifying the data generating process in the great majority of cases studied.

Keywords

Genetic Algorithm ARMA Model Time Series Prediction Binary Digit Simulated Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Tommaso Minerva
    • 1
  • Irene Poli
    • 2
  1. 1.Faculty of EconomicsUniversity of Modena and Reggio EmiliaModenaItaly
  2. 2.Department of StatisticsUniversità Ca’ FoscariVeneziaItaly

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