Multiple change point detection and validation in autoregressive time series data

Lijing Ma, Andrew J. Grant, Georgy Sofronov

    Research output: Contribution to journalConference paperpeer-review

    12 Citations (Scopus)
    50 Downloads (Pure)


    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.

    Original languageEnglish
    Pages (from-to)1507-1528
    Number of pages22
    JournalStatistical Papers
    Issue number4
    Early online date13 Jul 2020
    Publication statusPublished - Aug 2020
    EventWorkshop on Change Point Detection Limit Theorems, Algorithms, and Applications in Life Sciences - Greifswald, Germany
    Duration: 8 Jul 201910 Jul 2019

    Bibliographical note

    Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


    • Changepoint detection
    • Autoregressive time series
    • Likelihood ratio scan statistics
    • Multiple testing problems


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