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On the Selection of Subset Bilinear Time Series Models: a Genetic Algorithm Approach

Author

Listed:
  • Cathy W. S. Chen

    (Feng Chia University)

  • Tsai-Hung Cherng

    (Feng Chia University)

  • Berlin Wu

    (National Chengchi University)

Abstract

Summary This paper explores the idea of using a Genetic algorithm (GA) to solve the problem of subset model selection within the class of bilinear time series processes. The research is based on the concept of evolution theory as well as that of survival of the fittest. We use the AIC, BIG or SBC criteria as the adaptive functions to measure the degree of fitness. During the GA process, the best-fitted population is selected and certain characteristics are translated into the next generation. Simulation results demonstrate that genetic-based learning can effectively work out a pattern of the underlying time series. Finally, we illustrate how the GA can be applied successfully to subset selection in a bilinear time series via several examples and a simulation study.

Suggested Citation

  • Cathy W. S. Chen & Tsai-Hung Cherng & Berlin Wu, 2001. "On the Selection of Subset Bilinear Time Series Models: a Genetic Algorithm Approach," Computational Statistics, Springer, vol. 16(4), pages 505-517, December.
    • Handle: RePEc:spr:compst:v:16:y:2001:i:4:d:10.1007_s180-001-8327-9
    DOI: 10.1007/s180-001-8327-9
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    References listed on IDEAS

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    1. M. M. Gabr & T. Subba Rao, 1981. "The Estimation And Prediction Of Subset Bilinear Time Series Models With Applications," Journal of Time Series Analysis, Wiley Blackwell, vol. 2(3), pages 155-171, May.
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    Cited by:

    1. Francesco Battaglia & Mattheos K. Protopapas, 2011. "Time‐varying multi‐regime models fitting by genetic algorithms," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(3), pages 237-252, May.
    2. Roberto Baragona & Francesco Battaglia & Domenico Cucina, 2004. "Estimating threshold subset autoregressive moving-average models by genetic algorithms," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 39-61.

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