abstract = "In this paper we present a case study: the application
of some conceptually different approaches to the
problem of identifying a model for a hydrological time
series. The problem is particularly challenging, due to
the size of the time series and more importantly, to
the many complex phenomena that influence such time
series and that reflect in the characteristics of the
data. We use well established statistical methods to
detect change points in the time series, and we model
the subseries obtained by ARIMA, GEP and the adaptive
variant and a combination of the two. The models
obtained state the efficiency of combining pure
statistical tests and methods with heuristic
approaches.",