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Change-Point Detection in Autoregressive Processes via the Cross-Entropy Method

Department of Mathematics and Statistics, Macquarie University, Sydney, NSW 2109, Australia
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Algorithms 2020, 13(5), 128; https://doi.org/10.3390/a13050128
Received: 10 April 2020 / Revised: 18 May 2020 / Accepted: 18 May 2020 / Published: 20 May 2020
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. View Full-Text
Keywords: time series; change-point; Minimum Description Length; Cross-Entropy algorithm time series; change-point; Minimum Description Length; Cross-Entropy algorithm
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Ma, L.; Sofronov, G. Change-Point Detection in Autoregressive Processes via the Cross-Entropy Method. Algorithms 2020, 13, 128.

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