Retrieving Time-Series Differences Using Natural Language Queries

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.

@article{dohi2025_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 }
}