用多智能体自动标注时序数据，跨领域效果更好。
Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation
- 设计两个智能体：通用与领域专用，分别学习共性与特定术语
- 在多个真实和合成数据集上优于现有方法，生成高质量标注
- 适合需要快速构建领域时序标注的工业、医疗等场景
时序数据广泛存在于制造、金融、医疗等领域。高质量标注对理解时序数据及支持下游任务至关重要，但在关键领域获取标注仍具挑战。本文提出TESSA，一种多智能体系统，用于自动为时序数据生成通用与领域特定的标注。TESSA包含两个智能体：通用标注智能体利用跨源领域的时序特征与文本特征，捕捉共性模式；领域专用智能体则基于目标域的少量标注，学习领域术语并生成针对性标注。在多个合成与真实世界数据集上的实验表明，TESSA能有效生成高质量标注，性能优于现有方法。
原文摘要 · Abstract (English)
Time series data is ubiquitous across various domains, including manufacturing, finance, and healthcare. High-quality annotations are essential for effectively understanding time series and facilitating downstream tasks; however, obtaining such annotations is challenging, particularly in mission-critical domains. In this paper, we propose TESSA, a multi-agent system designed to automatically generate both general and domain-specific annotations for time series data. TESSA introduces two agents: a general annotation agent and a domain-specific annotation agent. The general agent captures common patterns and knowledge across multiple source domains, leveraging both time-series-wise and text-wise features to generate general annotations. Meanwhile, the domain-specific agent utilizes limited annotations from the target domain to learn domain-specific terminology and generate targeted annotations. Extensive experiments on multiple synthetic and real-world datasets demonstrate that TESSA effectively generates high-quality annotations, outperforming existing methods.
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