Finding Novel Multivariate Relationships in Time Series Data: Applications to Climate and Neuroscience
2018-02-12
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Finding Novel Multivariate Relationships in Time Series Data: Applications to Climate and Neuroscience
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2018-02-12
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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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Agrawal, Saurabh; Steinbach, Michael; Boley, Daniel; Liess, Stefan; Chatterjee, Snigdhansu; Kumar, Vipin; Atluri, Gowtham. (2018). Finding Novel Multivariate Relationships in Time Series Data: Applications to Climate and Neuroscience. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/216019.
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