Mining novel multivariate relationships in time series data using correlation networks

Saurabh Agrawal, Michael Steinbach, Daniel Boley, Snigdhansu Chatterjee, Gowtham Atluri, Anh The Dang, Stefan Liess, Vipin Kumar

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number8693798
Pages (from-to)1798-1811
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume32
Issue number9
DOIs
StatePublished - Sep 1 2020

Bibliographical note

Funding Information:
The authors would like to thank Siddhant Agrawal, Department of Mathematics, University of Michigan, for invaluable discussions. This work was supported by NSF grants IIS-1029771 and IIS-1319749 and NASA grant 14-CMAC14-0010. Access to the computing facilities was provided by the University of Minnesota’s Supercomputing Institute.

Publisher Copyright:
© 1989-2012 IEEE.

Keywords

  • Multivariate linear patterns
  • climate teleconnections
  • correlation mining
  • fMRI
  • spatio-temporal

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