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arXiv · 2101.04334

Change-point detection using spectral PCA for multivariate time series

Shuhao Jiao·Tong Shen·Zhaoxia Yu·Hernando Ombao
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Abstract

We propose a two-stage approach Spec PC-CP to identify change points in multivariate time series. In the first stage, we obtain a low-dimensional summary of the high-dimensional time series by Spectral Principal Component Analysis (Spec-PCA). In the second stage, we apply cumulative sum-type test on the Spectral PCA component using a binary segmentation algorithm. Compared with existing approaches, the proposed method is able to capture the lead-lag relationship in time series. Our simulations demonstrate that the Spec PC-CP method performs significantly better than competing methods for detecting change points in high-dimensional time series. The results on epileptic seizure EEG data and stock data also indicate that our new method can efficiently {detect} change points corresponding to the onset of the underlying events.

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Shuhao Jiao, Tong Shen, Zhaoxia Yu, Hernando Ombao. 2021-01-12. Change-point detection using spectral PCA for multivariate time series. https://arxiv.org/abs/2101.04334

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