A Fast-Optimal Guaranteed Algorithm For Learning Sub-Interval Relationships in Time Series

Abstract

Traditional approaches focus on finding relationships between two entire time series, however, many interesting relationships exist in small sub-intervals of time and remain feeble during other sub-intervals. We define the notion of a sub-interval relationship (SIR) to capture such interactions that are prominent only in certain sub-intervals of time. To that end, we propose a fast-optimal guaranteed algorithm to find most interesting SIR relationship in a pair of time series. Lastly, we demonstrate the utility of our method in climate science domain based on a real-world dataset along with its scalability scope and obtain useful domain insights.

Accepted at The Thirty-sixth International Conference on Machine Learning (ICML 2019), Time Series Workshop. arXiv admin note: substantial text overlap with arXiv:1802.06095