Full Citation
Title: Mining novel multivariate relationships in time series data using correlation networks
Citation Type: Journal Article
Publication Year: 2020
ISBN:
ISSN: 15582191
DOI: 10.1109/TKDE.2019.2911681
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Abstract: In many domains, there is significant interest in capturing novel relationships between time series that represent activities recorded at different nodes of a highly complex system. In this paper, we introduce multipoles, a novel class of linear relationships between more than two time series. A multipole is a set of time series that have strong linear dependence among themselves, with the requirement that each time series makes a significant contribution to the linear dependence. We demonstrate that most interesting multipoles can be identified as cliques of negative correlations in a correlation network. Such cliques are typically rare in a real-world correlation network, which allows us to find almost all multipoles efficiently using a clique-enumeration approach. Using our proposed framework, we demonstrate the utility of multipoles in discovering new physical phenomena in two scientific domains: climate science and neuroscience. In particular, we discovered several multipole relationships that are reproducible in multiple other independent datasets and lead to novel domain insights.
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Authors: Agrawal, Saurabh; Steinbach, Michael; Boley, Daniel; Chatterjee, Snigdhansu; Atluri, Gowtham; Dang, Anh The; Liess, Stefan; Kumar, Vipin
Periodical (Full): IEEE Transactions on Knowledge and Data Engineering
Issue: 9
Volume: 32
Pages: 1798-1811
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