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Periodic autoregressive model identification using genetic algorithms

Domenii publicaţii > Matematica + Tipuri publicaţii > Articol în revistã ştiinţificã

Autori: Eugen Ursu, Kamil Feridun Turkman

Editorial: Journal of Time Series Analysis, 33 (3), p.398-405, 2012.

Rezumat:

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.

Cuvinte cheie: Journal of Time Series Analysis