  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.
