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|
|Number of pages||10|
|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
|Name||Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Other||23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017|
|Period||8/13/17 → 8/17/17|
Bibliographical noteFunding 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.
- Climate teleconnections
- Correlation mining
- Multivariate linear patterns