Tripoles: A new class of relationships in time series data

Saurabh Agrawal; Gowtham Atluri; Anuj Karpatne; William Haltom; Stefan Liess; Singdhansu B Chatterjee; Vipin Kumar

doi:10.1145/3097983.3098099

Tripoles: A new class of relationships in time series data

Saurabh Agrawal, Gowtham Atluri, Anuj Karpatne, William Haltom, Stefan Liess, Singdhansu B Chatterjee, Vipin Kumar

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

Mining relationships in time series data is of immense interest to several disciplines such as neuroscience, climate science, and transportation. Traditional approaches for mining relationships focus on discovering pair-wise relationships in the data. In this work, we define a novel relationship pattern involving three interacting time series, which we refer to as a tripole. We show that tripoles capture interesting relationship patterns in the data that are not possible to be captured using traditionally studied pair-wise relationships. We demonstrate the utility of tripoles in multiple real-world datasets from various domains including climate science and neuroscience. In particular, our approach is able to discover tripoles that are statistically significant, reproducible across multiple independent data sets, and lead to novel domain insights.

Original language	English (US)
Title of host publication	KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	697-706
Number of pages	10
ISBN (Electronic)	9781450348874
DOIs	https://doi.org/10.1145/3097983.3098099
State	Published - Aug 13 2017
Event	23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017 - Halifax, Canada Duration: Aug 13 2017 → Aug 17 2017

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume	Part F129685

Other

Other	23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017
Country/Territory	Canada
City	Halifax
Period	8/13/17 → 8/17/17

Bibliographical note

Funding Information:
We thank reviewers for helpful comments and suggestions. Œis work was supported by NSF grant IIS-1029771 and NASA grant 14-CMAC14-0010. Access to the computing facilities was provided by the University of Minnesota Supercomputing Institute.

Publisher Copyright:
© 2017 ACM.

Keywords

Climate teleconnections
Correlation mining
FMRI
Multivariate linear patterns
Spatio-temporal

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Agrawal, S., Atluri, G., Karpatne, A., Haltom, W., Liess, S., Chatterjee, S. B., & Kumar, V. (2017). Tripoles: A new class of relationships in time series data. In KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 697-706). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. Part F129685). Association for Computing Machinery. https://doi.org/10.1145/3097983.3098099

Tripoles: A new class of relationships in time series data. / Agrawal, Saurabh; Atluri, Gowtham; Karpatne, Anuj et al.
KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2017. p. 697-706 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. Part F129685).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Agrawal, S, Atluri, G, Karpatne, A, Haltom, W, Liess, S , Chatterjee, SB & Kumar, V 2017, Tripoles: A new class of relationships in time series data. in KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. Part F129685, Association for Computing Machinery, pp. 697-706, 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017, Halifax, Canada, 8/13/17. https://doi.org/10.1145/3097983.3098099

Agrawal S, Atluri G, Karpatne A, Haltom W, Liess S , Chatterjee SB et al. Tripoles: A new class of relationships in time series data. In KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2017. p. 697-706. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3097983.3098099

Agrawal, Saurabh ; Atluri, Gowtham ; Karpatne, Anuj et al. / Tripoles : A new class of relationships in time series data. KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2017. pp. 697-706 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

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Tripoles: A new class of relationships in time series data

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