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Multiple change point detection and validation in autoregressive time series data

Author

Listed:
  • Lijing Ma

    (Macquarie University)

  • Andrew J. Grant

    (University of Cambridge)

  • Georgy Sofronov

    (Macquarie University)

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.

Suggested Citation

  • Lijing Ma & Andrew J. Grant & Georgy Sofronov, 2020. "Multiple change point detection and validation in autoregressive time series data," Statistical Papers, Springer, vol. 61(4), pages 1507-1528, August.
    • Handle: RePEc:spr:stpapr:v:61:y:2020:i:4:d:10.1007_s00362-020-01198-w
    DOI: 10.1007/s00362-020-01198-w
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    References listed on IDEAS

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    Cited by:

    1. Georgy Sofronov & Martin Wendler & Volkmar Liebscher, 2020. "Editorial for the special issue: Change point detection," Statistical Papers, Springer, vol. 61(4), pages 1347-1349, August.
    2. Julius Juodakis & Stephen Marsland, 2023. "Epidemic changepoint detection in the presence of nuisance changes," Statistical Papers, Springer, vol. 64(1), pages 17-39, February.
    3. Joseph Ngatchou-Wandji & Echarif Elharfaoui & Michel Harel, 2022. "On change-points tests based on two-samples U-Statistics for weakly dependent observations," Statistical Papers, Springer, vol. 63(1), pages 287-316, February.

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