This paper develops a procedure for identifying multiregime Periodic AutoRegressive (PAR) models. In each regime a possibly dif- ferent PAR model is built, for which changes can be due to the seasonal means, the autocorrelation structure or the variances. Number and locations of changepoints which subdivide the time span are detected by means of Genetic Algorithms (GAs), that optimize an identification criterion. The method is evaluated by means of simulation studies, and is then employed to analyze shrimp fishery data.
Identification of multiregime periodic autoregressive models by genetic algorithms / Cucina, Domenico; Rizzo, Manuel; Ursu, Eugen. - (2018), pp. 396-407. (Intervento presentato al convegno International conference on Time Series and Forecasting 2018 (ITISE 2018) tenutosi a Granada; Spain).
Identification of multiregime periodic autoregressive models by genetic algorithms
Domenico CucinaPrimo
;Manuel RizzoSecondo
;
2018
Abstract
This paper develops a procedure for identifying multiregime Periodic AutoRegressive (PAR) models. In each regime a possibly dif- ferent PAR model is built, for which changes can be due to the seasonal means, the autocorrelation structure or the variances. Number and locations of changepoints which subdivide the time span are detected by means of Genetic Algorithms (GAs), that optimize an identification criterion. The method is evaluated by means of simulation studies, and is then employed to analyze shrimp fishery data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
