Contextual time serios change detection

Xi C. Chen, Karsten Steinhaeuser, Shyam Boriah, Singdhansu B Chatterjee, Vipin Kumar

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

Abstract

Time series data are common in a variety of fields ranging from economics to medicine and manufacturing. As a result, time series analysis and modeling has become an active research area in statistics and data mining. In this paper, we focus on a type of change we call contextual time series change (CTC) and propose a novel two-stage algorithm to address it. In contrast to traditional change detection methods, which consider each time series separately, CTC is defined as a change relative to the behavior of a group of related time series. As a result, our proposed method is able to identify novel types of changes not found by other algorithms. We demonstrate the unique capabilities of our approach with several case studies on real-world datasets from the financial and Earth science domains.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2013, SMD 2013
PublisherSociety for Industrial and Applied Mathematics Publications
Pages503-511
Number of pages9
ISBN (Electronic)9781627487245
StatePublished - Jan 1 2013
Event13th SIAM International Conference on Data Mining, SMD 2013 - Austin, United States
Duration: May 2 2013May 4 2013

Publication series

NameSIAM International Conference on Data Mining 2013, SMD 2013

Other

Other13th SIAM International Conference on Data Mining, SMD 2013
CountryUnited States
CityAustin
Period5/2/135/4/13

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Chen, X. C., Steinhaeuser, K., Boriah, S., Chatterjee, S. B., & Kumar, V. (2013). Contextual time serios change detection. In SIAM International Conference on Data Mining 2013, SMD 2013 (pp. 503-511). (SIAM International Conference on Data Mining 2013, SMD 2013). Society for Industrial and Applied Mathematics Publications.