Mining Sub-Interval Relationships In Time Series Data

TL;DR

This paper introduces a new method for discovering significant sub-interval relationships in time series data, revealing complex, localized interactions that traditional methods often miss, with applications demonstrated in climate science and neuroscience.

Contribution

The paper proposes a novel, efficient approach to identify and analyze sub-interval relationships in time series data, addressing limitations of traditional whole-series analysis.

Findings

Identified statistically significant sub-interval relationships in real-world datasets.

Demonstrated the scalability and efficiency of the proposed approach.

Some discovered relationships have meaningful physical interpretations.

Abstract

Time-series data is being increasingly collected and stud- ied in several areas such as neuroscience, climate science, transportation, and social media. Discovery of complex patterns of relationships between individual time-series, using data-driven approaches can improve our understanding of real-world systems. While traditional approaches typically study relationships between two entire time series, many interesting relationships in real-world applications exist in small sub-intervals of time while remaining absent or feeble during other sub-intervals. In this paper, we define the notion of a sub-interval relationship (SIR) to capture inter- actions between two time series that are prominent only in certain sub-intervals of time. We propose a novel and efficient approach to find most interesting SIR in a pair of time series. We evaluate our proposed approach on two real-world datasets from climate science and neuroscience domain and demonstrated the scalability and computational efficiency of our proposed approach. We further evaluated our discovered SIRs based on a randomization based procedure. Our results indicated the existence of several such relationships that are statistically significant, some of which were also found to have physical interpretation.