Identification of multiregime periodic autotregressive models by genetic algorithms
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EN
Communication dans un congrès avec actes
Este ítem está publicado en
International Conference of Time Series and Forecasting, 2018, Grenade.
Resumen en inglés
.This paper develops a procedure for identifying multiregimePeriodic AutoRegressive (PAR) models. In each regime a possibly dif-ferent PAR model is built, for which changes can be due to the seasonalmeans, the autocorrelation ...Leer más >
.This paper develops a procedure for identifying multiregimePeriodic AutoRegressive (PAR) models. In each regime a possibly dif-ferent PAR model is built, for which changes can be due to the seasonalmeans, the autocorrelation structure or the variances. Number and lo-cations of changepoints which subdivide the time span are detected bymeans of Genetic Algorithms (GAs), that optimize an identification cri-terion. The method is evaluated by means of simulation studies, and isthen employed to analyze shrimp fishery data.< Leer menos
Palabras clave en inglés
Seasonality
Structural changes
Genetic algorithm
Centros de investigación