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

ACL ARR 2025 May Submission1683 Authors

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. Our code and data are available at https://anonymous.4open.science/r/TESSA-8B7D.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: Multi-agent System; Large Language Models; Time Series Annotation; Time Series Analysis

Contribution Types: NLP engineering experiment, Data analysis

Languages Studied: English

Keywords: Multi-agent System, Large Language Models, Time Series Annotation, Time Series Analysis

Submission Number: 1683