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Periodic autoregressive model identification using genetic algorithms
Author(s) -
Ursu Eugen,
Turkman Kamil Feridun
Publication year - 2012
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.2011.00772.x
Subject(s) - autoregressive model , star model , bayesian information criterion , model selection , mathematics , selection (genetic algorithm) , nonlinear autoregressive exogenous model , bayesian probability , algorithm , series (stratigraphy) , identification (biology) , information criteria , setar , autoregressive–moving average model , autoregressive integrated moving average , time series , genetic algorithm , computer science , mathematical optimization , artificial intelligence , statistics , paleontology , botany , biology
Periodic autoregressive (PAR) models extend the classical autoregressive models by allowing the parameters to vary with seasons. Selecting PAR time‐series models can be computationally expensive, and the results are not always satisfactory. In this article, we propose a new automatic procedure to the model selection problem by using the genetic algorithm. The Bayesian information criterion is used as a tool to identify the order of the PAR model. The success of the proposed procedure is illustrated in a small simulation study, and an application with monthly data is presented.