Contextual time series change detection

Xi C. Chen; Karsten Steinhaeuser; Shyam Boriah; Snigdhansu Chatterjeey; Vipin Kumar

doi:10.1137/1.9781611972832.56

Contextual time series change detection

Xi C. Chen
, Karsten Steinhaeuser
, Shyam Boriah
, Snigdhansu Chatterjeey
, Vipin Kumar

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

17 Scopus citations

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 language	English (US)
Title of host publication	Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013
Editors	Joydeep Ghosh, Zoran Obradovic, Jennifer Dy, Zhi-Hua Zhou, Chandrika Kamath, Srinivasan Parthasarathy
Publisher	Siam Society
Pages	503-511
Number of pages	9
ISBN (Electronic)	9781611972627
DOIs	https://doi.org/10.1137/1.9781611972832.56
State	Published - 2013
Event	SIAM International Conference on Data Mining, SDM 2013 - Austin, United States Duration: May 2 2013 → May 4 2013

Publication series

Name	Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013

Other

Other	SIAM International Conference on Data Mining, SDM 2013
Country/Territory	United States
City	Austin
Period	5/2/13 → 5/4/13

Bibliographical note

Publisher Copyright:
Copyright © SIAM.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

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10.1137/1.9781611972832.56

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Chen, X. C., Steinhaeuser, K., Boriah, S., Chatterjeey, S., & Kumar, V. (2013). Contextual time series change detection. In J. Ghosh, Z. Obradovic, J. Dy, Z.-H. Zhou, C. Kamath, & S. Parthasarathy (Eds.), Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013 (pp. 503-511). (Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013). Siam Society. https://doi.org/10.1137/1.9781611972832.56

Contextual time series change detection. / Chen, Xi C.; Steinhaeuser, Karsten; Boriah, Shyam et al.
Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013. ed. / Joydeep Ghosh; Zoran Obradovic; Jennifer Dy; Zhi-Hua Zhou; Chandrika Kamath; Srinivasan Parthasarathy. Siam Society, 2013. p. 503-511 (Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, XC, Steinhaeuser, K, Boriah, S, Chatterjeey, S & Kumar, V 2013, Contextual time series change detection. in J Ghosh, Z Obradovic, J Dy, Z-H Zhou, C Kamath & S Parthasarathy (eds), Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013. Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013, Siam Society, pp. 503-511, SIAM International Conference on Data Mining, SDM 2013, Austin, United States, 5/2/13. https://doi.org/10.1137/1.9781611972832.56

Chen XC, Steinhaeuser K, Boriah S, Chatterjeey S, Kumar V. Contextual time series change detection. In Ghosh J, Obradovic Z, Dy J, Zhou ZH, Kamath C, Parthasarathy S, editors, Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013. Siam Society. 2013. p. 503-511. (Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013). doi: 10.1137/1.9781611972832.56

Chen, Xi C. ; Steinhaeuser, Karsten ; Boriah, Shyam et al. / Contextual time series change detection. Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013. editor / Joydeep Ghosh ; Zoran Obradovic ; Jennifer Dy ; Zhi-Hua Zhou ; Chandrika Kamath ; Srinivasan Parthasarathy. Siam Society, 2013. pp. 503-511 (Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013).

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AB - 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.

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Contextual time series change detection

Abstract

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