用大语言模型解码时间序列：面向跨域标注的多智能体框架
Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation
AI总结：
针对时间序列高质量标注获取难的问题，提出多智能体系统TESSA，通过通用与领域特定双智能体协作生成跨域标注，实验证明其效果优于现有方法。
摘要：
时间序列数据广泛存在于制造、金融、医疗等多个领域。高质量标注对于有效理解时间序列、支撑下游任务至关重要，但获取这类标注难度较大，在关键任务领域尤其如此。本文提出TESSA——一种多智能体系统，可自动生成时间序列数据的通用标注与领域特定标注。TESSA包含两个智能体：通用标注智能体和领域特定标注智能体。通用智能体捕捉多个源域的共性模式与知识，同时利用时间序列维度和文本维度的特征生成通用标注；领域特定智能体则借助目标域的少量标注，学习领域专属术语并生成针对性标注。在多个合成数据集与真实数据集上的大量实验表明，TESSA可有效生成高质量标注，性能优于现有方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
