We present a model and a computational procedure for dealing with seasonality and regime changes in time series. In this work we are interested in time series which in addition to trend display seasonality in mean, in autocorrelation and in variance. These type of series appears in many areas, including hydrology, meteorology, economics and finance. The seasonality is accounted for by subset PAR modelling, for which each season follows a possibly different Autoregressive model. Levels, trend, autoregressive parameters and residual variances are allowed to change their values at fixed unknown times. The identification of number and location of structural changes, as well as $PAR$ lags indicators, is based on Genetic Algorithms, which are suitable because of high dimensionality of the discrete search space. An application to Italian industrial production index time series is also proposed.

Periodic autoregressive models with multiple structural changes by genetic algorithms / Battaglia, Francesco; Cucina, Domenico; Rizzo, Manuel. - STAMPA. - (2018), pp. 107-110. (Intervento presentato al convegno Mathematical and Statistical Methods for Actuarial Sciences and Finance (MAF 2018) Mathematical and Statistical Methods for Actuarial Sciences and Finance (MAF 2018) tenutosi a Madrid; Spain) [10.1007/978-3-319-89824-7].

Periodic autoregressive models with multiple structural changes by genetic algorithms

Francesco Battaglia;Domenico Cucina
;
Manuel Rizzo
2018

Abstract

We present a model and a computational procedure for dealing with seasonality and regime changes in time series. In this work we are interested in time series which in addition to trend display seasonality in mean, in autocorrelation and in variance. These type of series appears in many areas, including hydrology, meteorology, economics and finance. The seasonality is accounted for by subset PAR modelling, for which each season follows a possibly different Autoregressive model. Levels, trend, autoregressive parameters and residual variances are allowed to change their values at fixed unknown times. The identification of number and location of structural changes, as well as $PAR$ lags indicators, is based on Genetic Algorithms, which are suitable because of high dimensionality of the discrete search space. An application to Italian industrial production index time series is also proposed.
2018
Mathematical and Statistical Methods for Actuarial Sciences and Finance (MAF 2018) Mathematical and Statistical Methods for Actuarial Sciences and Finance (MAF 2018)
time series; seasonality; nonstationarity
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Periodic autoregressive models with multiple structural changes by genetic algorithms / Battaglia, Francesco; Cucina, Domenico; Rizzo, Manuel. - STAMPA. - (2018), pp. 107-110. (Intervento presentato al convegno Mathematical and Statistical Methods for Actuarial Sciences and Finance (MAF 2018) Mathematical and Statistical Methods for Actuarial Sciences and Finance (MAF 2018) tenutosi a Madrid; Spain) [10.1007/978-3-319-89824-7].
File allegati a questo prodotto
File Dimensione Formato  
Battaglia_Periodic-autoregressive_2018.pdf

solo gestori archivio

Tipologia: Documento in Post-print (versione successiva alla peer review e accettata per la pubblicazione)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 34.85 kB
Formato Adobe PDF
34.85 kB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1130433
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact