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

## TL;DR

This paper introduces a fast, guaranteed algorithm to identify significant sub-interval relationships in time series data, enabling detection of transient interactions that are not apparent over entire series.

## Contribution

The paper presents a novel, efficient algorithm for discovering the most interesting sub-interval relationships in time series, with proven optimality guarantees.

## Key findings

- Effective in climate science data analysis
- Scalable to large datasets
- Reveals transient 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.