Supplement for "Contextual Time Series Change Detection"
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Supplement for "Contextual Time Series Change Detection"
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2013-01-25
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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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Chen, Xi; Steinhaeuser, Karsten; Boriah, Shyam; Chatterjee, Snigdhansu; Kumar, Vipin. (2013). Supplement for "Contextual Time Series Change Detection". Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215905.
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