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It is quite common that the structure of a time series changes abruptly.Identifying these change points and describing the model structure in thesegments between these change points is of interest. In this paper, time seriesdata is modelled assuming each segment is an autoregressive time series withpossibly different autoregressive parameters. This is achieved using two mainsteps. The first step is to use a likelihood ratio scan based estimationtechnique to identify these potential change points to segment the time series.Once these potential change points are identified, modified parametric spectraldiscrimination tests are used to validate the proposed segments. A numericalstudy is conducted to demonstrate the performance of the proposed method acrossvarious scenarios and compared against other contemporary techniques.

Multiple Change Point Detection and Validation in Autoregressive Time Series Data