Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation

• URL: http://arxiv.org/abs/2410.17462v1
• Date: Tue, 22 Oct 2024 22:43:14 GMT
• Title: Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation
• Authors: Minhua Lin, Zhengzhang Chen, Yanchi Liu, Xujiang Zhao, Zongyu Wu, Junxiang Wang, Xiang Zhang, Suhang Wang, Haifeng Chen,
• Abstract summary: TESSA is a multi-agent system designed to automatically generate both general and domain-specific annotations for time series data. General agent captures common patterns and knowledge across multiple source domains, leveraging both time-series-wise and text-wise features. The domain-specific agent utilizes limited annotations from the target domain to learn domain-specific terminology and generate targeted annotations.
• Score: 56.78444462585225
• License:
• Abstract: 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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