Subset ARMA Model Identification Using Genetic Algorithms

Carlo Gaetan

Journal of Time Series Analysis, 2000, vol. 21, issue 5, 559-570

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.

Date: 2000
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