A robust change-point detection method by eliminating sparse noises from time series

Kun Qin, Lei Sun, Bo Liu, Yuan Fan, Kar Ann Toh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Singular Spectrum Transform (SST) is a fundamental subspace analysis technique which has been widely adopted for solving change-point detection (CPD) problems in information security applications. However, the performance of a SST based CPD algorithm is limited to the lack of robustness to corrupted observations with large noises in practice. Based on the observation that large noises in practical time series are generally sparse, in this paper, we study a combination of Robust Principal Component Analysis (RPCA) and SST to obtain a robust CPD algorithm dealing with sparse large noises. The sparse large noises are to be eliminated from observation trajectory matrices by performing a low-rank matrix recovery procedure of RPCA. The noise-eliminated matrices are then used to extract SST subspaces for CPD. The effectiveness of the proposed method is demonstrated through experiments based on both synthetic and real-world datasets. Experimental results show that the proposed method outperforms the competing state-of-the-arts in terms of detection accuracy for time series with sparse large noises.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 3rd International Conference on Data Science in Cyberspace, DSC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages146-152
Number of pages7
ISBN (Electronic)9781538642108
DOIs
Publication statusPublished - 2018 Jul 16
Event3rd IEEE International Conference on Data Science in Cyberspace, DSC 2018 - Guangzhou, Guangdong, China
Duration: 2018 Jun 182018 Jun 21

Publication series

NameProceedings - 2018 IEEE 3rd International Conference on Data Science in Cyberspace, DSC 2018

Other

Other3rd IEEE International Conference on Data Science in Cyberspace, DSC 2018
CountryChina
CityGuangzhou, Guangdong
Period18/6/1818/6/21

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

Fingerprint Dive into the research topics of 'A robust change-point detection method by eliminating sparse noises from time series'. Together they form a unique fingerprint.

  • Cite this

    Qin, K., Sun, L., Liu, B., Fan, Y., & Toh, K. A. (2018). A robust change-point detection method by eliminating sparse noises from time series. In Proceedings - 2018 IEEE 3rd International Conference on Data Science in Cyberspace, DSC 2018 (pp. 146-152). [8411850] (Proceedings - 2018 IEEE 3rd International Conference on Data Science in Cyberspace, DSC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSC.2018.00029