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 Cucina
Primo
;
Manuel Rizzo
Secondo
;
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.
2018
International conference on Time Series and Forecasting 2018 (ITISE 2018)
Seasonality, Structural changes, Genetic algorithm
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
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).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1178320
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