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