Kavli Affiliate: Xiang Zhang
| First 5 Authors: Minhua Lin, Zhengzhang Chen, Yanchi Liu, Xujiang Zhao, Zongyu Wu
| Summary:
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.
| Search Query: ArXiv Query: search_query=au:”Xiang Zhang”&id_list=&start=0&max_results=3