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High-dimensional time series are characterized by a large number ofmeasurements and complex dependence, and often involve abrupt change points. Wepropose a new procedure to detect change points in the mean of high-dimensionaltime series data. The proposed procedure incorporates spatial and temporaldependence of data and is able to test and estimate the change point occurredon the boundary of time series. We study its asymptotic properties under mildconditions. Simulation studies demonstrate its robust performance through thecomparison with other existing methods. Our procedure is applied to an fMRIdataset.

Change Point Detection in the Mean of High-Dimensional Time Series Data under Dependence