用自然语言精准检索时间序列中的关键片段。
LaSTR: Language-Driven Time-Series Segment Retrieval
- 基于Conformer的对比学习模型，共享文本与时间序列嵌入空间。
- 在多候选池下优于随机基线和CLIP，提升检索准确率与语义一致性。
- 适用于系统分析、故障诊断等需理解时序片段的场景。
高效搜索时间序列数据对系统分析至关重要，但现有方法通常依赖专家设计的相似性标准或全局序列描述。本文研究语言驱动的片段检索：给定自然语言查询，从大规模时间序列库中检索相关局部片段。通过TV2分割LOTSA窗口并使用GPT-5.2生成片段描述，构建大规模段落-字幕训练数据；随后在共享文本-时间序列嵌入空间中训练基于Conformer的对比检索模型。在保留测试集上，评估单正例检索性能，并结合SBERT与视觉语言模型（VLM）作为评判者，考察不同候选池规模下的表现。在所有设置中，LaSTR均优于随机和CLIP基线，显著提升排序质量与检索片段与查询意图之间的语义一致性。
原文摘要 · Abstract (English)
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 consistency (SBERT and VLM-as-a-judge) under multiple candidate pool sizes. Across all settings, LaSTR outperforms random and CLIP baselines, yielding improved ranking quality and stronger semantic agreement between retrieved segments and query intent.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。