Retrieving Time-Series Differences Using Natural Language Queries

2025/03/27 by Kota Dohi, Tomoya Nishida, Dohi, Kota +7
Computer Science · #Advanced Database Systems and Queries #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Time Series Analysis and Forecasting

paper · pdf · doi:10.48550/arxiv.2503.21378

openalex publication_date 2025/03/27 · openalex created_date 2025/10/11 · openalex updated_date 2026/07/28

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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