Change Point Detection with Copula Entropy based Two-Sample Test
This work addresses change point detection for time series analysis, presenting an incremental improvement by combining existing techniques.
The paper tackles the problem of detecting change points in time series by proposing a nonparametric multivariate method using a copula entropy-based two-sample test, achieving effectiveness verified through comparisons on simulated and real-world data like the Nile dataset.
Change point detection is a typical task that aim to find changes in time series and can be tackled with two-sample test. Copula Entropy is a mathematical concept for measuring statistical independence and a two-sample test based on it was introduced recently. In this paper we propose a nonparametric multivariate method for multiple change point detection with the copula entropy-based two-sample test. The single change point detection is first proposed as a group of two-sample tests on every points of time series data and the change point is considered as with the maximum of the test statistics. The multiple change point detection is then proposed by combining the single change point detection method with binary segmentation strategy. We verified the effectiveness of our method and compared it with the other similar methods on the simulated univariate and multivariate data and the Nile data.