AbstractMany nonstationary time series exhibit changes in the trend and seasonality structure, that may be modeled by splitting the time axis into different regimes. We propose multi-regime models where, inside each regime, the trend is linear and seasonality is explained by a Periodic Autoregressivemodel. In addition, for achieving parsimony, we allow season grouping, i.e. seasons may consist of one, two, or more consecutive observations. Identification is obtained by means of a Genetic Algorithm that minimizes an identification criterion
Generalized periodic autoregressive models for trend and seasonality varying time series / Battaglia, Francesco; Cucina, Domenico; Rizzo, Manuel. - (2018), pp. 192-200. (Intervento presentato al convegno 49th Scientific Meeting of the Italian Statistical Society tenutosi a Palermo).
Generalized periodic autoregressive models for trend and seasonality varying time series
Francesco Battaglia;Domenico Cucina;Manuel Rizzo
2018
Abstract
AbstractMany nonstationary time series exhibit changes in the trend and seasonality structure, that may be modeled by splitting the time axis into different regimes. We propose multi-regime models where, inside each regime, the trend is linear and seasonality is explained by a Periodic Autoregressivemodel. In addition, for achieving parsimony, we allow season grouping, i.e. seasons may consist of one, two, or more consecutive observations. Identification is obtained by means of a Genetic Algorithm that minimizes an identification criterionFile | Dimensione | Formato | |
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