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Subset ARMA Model Identification Using Genetic Algorithms

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  • Carlo Gaetan

Abstract

Subset models are often useful in the analysis of stationary time series. Although subset autoregressive models have received a lot of attention, the same attention has not been given to subset autoregressive moving‐average (ARMA) models, as their identification can be computationally cumbersome. In this paper we propose to overcome this disadvantage by employing a genetic algorithm. After encoding each ARMA model as a binary string, the iterative algorithm attempts to mimic the natural evolution of the population of such strings by allowing strings to reproduce, creating new models that compete for survival in the next population. The success of the proposed procedure is illustrated by showing its efficiency in identifying the true model for simulated data. An application to real data is also considered.

Suggested Citation

  • Carlo Gaetan, 2000. "Subset ARMA Model Identification Using Genetic Algorithms," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(5), pages 559-570, September.
    • Handle: RePEc:bla:jtsera:v:21:y:2000:i:5:p:559-570
    DOI: 10.1111/1467-9892.00198
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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. Francesco Battaglia & Mattheos Protopapas, 2012. "An analysis of global warming in the Alpine region based on nonlinear nonstationary time series models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(3), pages 315-334, August.
    3. 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.
    4. Francesco Battaglia & Mattheos K. Protopapas, 2010. "Multi-regime models for nonlinear nonstationary time series," Working Papers 026, COMISEF.
    5. PEREAU Jean-Christophe & URSU Eugen, 2015. "Application of periodic autoregressive process to the modeling of the Garonne river flows," Cahiers du GREThA (2007-2019) 2015-14, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
    6. Richard A. Davis & Thomas C. M. Lee & Gabriel A. Rodriguez‐Yam, 2008. "Break Detection for a Class of Nonlinear Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(5), pages 834-867, September.
    7. Francesco Battaglia & Mattheos Protopapas, 2012. "Multi–regime models for nonlinear nonstationary time series," Computational Statistics, Springer, vol. 27(2), pages 319-341, June.

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