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dc.contributor.authorJiao, Shuhao
dc.contributor.authorShen, Tong
dc.contributor.authorYu, Zhaoxia
dc.contributor.authorOmbao, Hernando
dc.date.accessioned2021-01-14T05:56:09Z
dc.date.available2021-01-14T05:56:09Z
dc.date.issued2021-01-12
dc.identifier.urihttp://hdl.handle.net/10754/666898
dc.description.abstractWe 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.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2101.04334
dc.rightsArchived with thanks to arXiv
dc.titleChange-point detection using spectral PCA for multivariate time series
dc.typePreprint
dc.contributor.departmentBiostatistics Group
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.eprint.versionPre-print
dc.contributor.institutionDepartment of Statistics, UC Irvine, USA.
dc.identifier.arxivid2101.04334
kaust.personJiao, Shuhao
kaust.personOmbao, Hernando
refterms.dateFOA2021-01-14T05:57:10Z


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