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    Change-point detection using spectral PCA for multivariate time series

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    Type
    Preprint
    Authors
    Jiao, Shuhao
    Shen, Tong
    Yu, Zhaoxia
    Ombao, Hernando cc
    KAUST Department
    Biostatistics Group
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Statistics Program
    Date
    2021-01-12
    Permanent link to this record
    http://hdl.handle.net/10754/666898
    
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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.
    Publisher
    arXiv
    arXiv
    2101.04334
    Additional Links
    https://arxiv.org/pdf/2101.04334
    Collections
    Preprints; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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