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.
Building ARMA models with genetic algorithms / Minerva, Tommaso; Poli, I.. - 2037:(2001), pp. 335-342. (Intervento presentato al convegno EvoWorkshops 2000 tenutosi a COMO, ITALY nel APR 18-20, 2001) [10.1007/3-540-45365-2_35].
Building ARMA models with genetic algorithms
MINERVA, Tommaso;
2001
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.
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