  It is quite common that the structure of a time series changes abruptly.
Identifying these change points and describing the model structure in the
segments between these change points is of interest. In this paper, time series
data is modelled assuming each segment is an autoregressive time series with
possibly different autoregressive parameters. This is achieved using two main
steps. The first step is to use a likelihood ratio scan based estimation
technique to identify these potential change points to segment the time series.
Once these potential change points are identified, modified parametric spectral
discrimination tests are used to validate the proposed segments. A numerical
study is conducted to demonstrate the performance of the proposed method across
various scenarios and compared against other contemporary techniques.
