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 language||English (US)|
|Number of pages||14|
|Journal||IEEE Transactions on Knowledge and Data Engineering|
|State||Published - Sep 1 2020|
Bibliographical noteFunding 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.
- Multivariate linear patterns
- climate teleconnections
- correlation mining