Change Point Detection with Copula Entropy based Two-Sample Test
Jian Ma
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.