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.
View on arXiv@article{lin2025_2410.17462,
title={ Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation },
author={ Minhua Lin and Zhengzhang Chen and Yanchi Liu and Xujiang Zhao and Zongyu Wu and Junxiang Wang and Xiang Zhang and Suhang Wang and Haifeng Chen },
journal={arXiv preprint arXiv:2410.17462},
year={ 2025 }
}