Retrieving Time-Series Differences Using Natural Language Queries
Abstract
Effectively searching time-series data is essential for system analysis; however, traditional methods often require domain expertise to define search criteria. Recent advancements have enabled natural language-based search, but these methods struggle to handle differences between time-series data. To address this limitation, we propose a natural language query-based approach for retrieving pairs of time-series data based on differences specified in the query. Specifically, we define six key characteristics of differences, construct a corresponding dataset, and develop a contrastive learning-based model to align differences between time-series data with query texts. Experimental results demonstrate that our model achieves an overall mAP score of 0.994 in retrieving time-series pairs.
Cite
@article{arxiv.2503.21378,
title = {Retrieving Time-Series Differences Using Natural Language Queries},
author = {Kota Dohi and Tomoya Nishida and Harsh Purohit and Takashi Endo and Yohei Kawaguchi},
journal= {arXiv preprint arXiv:2503.21378},
year = {2025}
}