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arXiv · 1906.01450

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

Saurabh Agrawal·Saurabh Verma·Anuj Karpatne·Stefan Liess·Snigdhansu Chatterjee·Vipin Kumar
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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.

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Saurabh Agrawal, Saurabh Verma, Anuj Karpatne, Stefan Liess, Snigdhansu Chatterjee, Vipin Kumar. 2019-06-03. A Fast-Optimal Guaranteed Algorithm For Learning Sub-Interval Relationships in Time Series. https://arxiv.org/abs/1906.01450

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