Multiple change point detection and validation in autoregressive time series data

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Ma, Lijing 
Grant, Andrew J. 
Sofronov, Georgy 

Abstract: 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.

Regular Article, Changepoint detection, Autoregressive time series, Likelihood ratio scan statistics, Multiple testing problems
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Statistical Papers
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Springer Berlin Heidelberg
Sir Henry Dale Fellowship jointly funded by the Welcome Trust and the Royal Society (204623/Z/16/Z)