Model Identi cation and Parameter Estimation of ARMA Models by Means of Evolutionary Algorithms
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چکیده
The eld of time series analysis and forecasting methods has signi cantly changed in the last decade due to the in uence of new knowledge in non-linear dynamics. New methods such as arti cial neural networks replaced traditional approaches which usually were appropriate for linear models only. Nevertheless, there are still applications where accurate estimations of linear processes, such as ARMA models, are su cient. However, the methods for this class of models were developed more than 20 years ago, with the restrictions of those days' computers in mind. The work described in this paper is an attempt to combine the ideas of the widely used BoxJenkins method [2] with new approaches to model identi cation and parameter estimation based on Evolutionary Algorithms, a class of probabilistic parameter optimization methods.
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تاریخ انتشار 1999