{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mining-novel-multivariate-relationships-in","title":"Mining Novel Multivariate Relationships in Time Series Data Using Correlation Networks","arxiv_id":"1810.02950","date":"2018-10-06","proceeding":null,"authors":["Saurabh Agrawal","Michael Steinbach","Daniel Boley","Snigdhansu Chatterjee","Gowtham Atluri","Anh The Dang","Stefan Liess","Vipin Kumar"],"abstract":"In many domains, there is significant interest in capturing novel\nrelationships between time series that represent activities recorded at\ndifferent nodes of a highly complex system. In this paper, we introduce\nmultipoles, a novel class of linear relationships between more than two time\nseries. A multipole is a set of time series that have strong linear dependence\namong themselves, with the requirement that each time series makes a\nsignificant contribution to the linear dependence. We demonstrate that most\ninteresting multipoles can be identified as cliques of negative correlations in\na correlation network. Such cliques are typically rare in a real-world\ncorrelation network, which allows us to find almost all multipoles efficiently\nusing a clique-enumeration approach. Using our proposed framework, we\ndemonstrate the utility of multipoles in discovering new physical phenomena in\ntwo scientific domains: climate science and neuroscience. In particular, we\ndiscovered several multipole relationships that are reproducible in multiple\nother independent datasets and lead to novel domain insights.","url_abs":"http://arxiv.org/abs/1810.02950v2","url_pdf":"http://arxiv.org/pdf/1810.02950v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mining-novel-multivariate-relationships-in","repo_url":"https://github.com/15saurabh16/Multipoles","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}