Ricerc@Sapienza

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

Many 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 Autoregressive model. In addition, for achieving parsimony, we allow season grouping, i.e. seasons may consists of one, two, or more consecutive observations.