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arXiv · 2503.21378

Retrieving Time-Series Differences Using Natural Language Queries

Kota Dohi·Tomoya Nishida·Harsh Purohit·Takashi Endo·Yohei Kawaguchi
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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.

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Kota Dohi, Tomoya Nishida, Harsh Purohit, Takashi Endo, Yohei Kawaguchi. 2025-03-27. Retrieving Time-Series Differences Using Natural Language Queries. https://arxiv.org/abs/2503.21378

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