LaSTR: Language-Driven Time-Series Segment Retrieval
Effectively searching time-series data is essential for system analysis, but existing methods often require expert-designed similarity criteria or rely on global, series-level descriptions. We study language-driven segment retrieval: given a natural language query, the goal is to retrieve relevant local segments from large time-series repositories. We build large-scale segment--caption training data by applying TV2-based segmentation to LOTSA windows and generating segment descriptions with GPT-5.2, and then train a Conformer-based contrastive retriever in a shared text--time-series embedding space. On a held-out test split, we evaluate single-positive retrieval together with caption-side...
| Authors | Kota Dohi, Harsh Purohit, Tomoya Nishida et al. |
|---|---|
| Primary category | cs.CL |
| Published | 28 February 2026 |
| Last updated at source | 28 February 2026 |
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