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|
|Number of pages||9|
|State||Published - 2013|
|Event||SIAM International Conference on Data Mining, SDM 2013 - Austin, United States|
Duration: May 2 2013 → May 4 2013
|Name||Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013|
|Other||SIAM International Conference on Data Mining, SDM 2013|
|Period||5/2/13 → 5/4/13|
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