Change-point detection in autoregressive processes via the Cross-Entropy method

Lijing Ma, Georgy Sofronov*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    1 Citation (Scopus)
    17 Downloads (Pure)

    Abstract

    It is very often the case that at some moment a time series process abruptly changes its underlying structure and, therefore, it is very important to accurately detect such change-points. In this problem, which is called a change-point (or break-point) detection problem, we need to find a method that divides the original nonstationary time series into a piecewise stationary segments. In this paper, we develop a flexible method to estimate the unknown number and the locations of change-points in autoregressive time series. In order to find the optimal value of a performance function, which is based on the Minimum Description Length principle, we develop a Cross-Entropy algorithm for the combinatorial optimization problem. Our numerical experiments show that the proposed approach is very efficient in detecting multiple change-points when the underlying process has moderate to substantial variations in the mean and the autocorrelation coefficient. We also apply the proposed method to real data of daily AUD/CNY exchange rate series from 2 January 2018 to 24 March 2020.
    Original languageEnglish
    Article number128
    Pages (from-to)1-16
    Number of pages16
    JournalAlgorithms
    Volume13
    Issue number5
    DOIs
    Publication statusPublished - May 2020

    Bibliographical note

    Copyright the Author(s) 2020. 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.

    Keywords

    • time series
    • change-point
    • Minimum Description Length
    • Cross-Entropy algorithm

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