用大模型分析时序图和描述，自动识别异常检测的误报
Refining Time Series Anomaly Detectors using Large Language Models
- 结合时序图与数据生成描述，让大模型判断异常真伪
- 能有效减少人工审核工作量，降低误报率
- 适合需要高效维护异常检测系统的工业场景
时序异常检测（TSAD）在金融、医疗、制造等领域广泛应用。尽管已有多种自动化检测方法，但人工仍需审核和确认检测结果的准确性。本文研究利用多模态大语言模型（LLM）部分自动化该过程。发现通过结合时序图的视觉观察与数据生成过程的文本描述，LLMs 能有效识别误报。借助大模型能力，可显著减少维护时序异常检测系统所需的人工投入。
原文摘要 · Abstract (English)
Time series anomaly detection (TSAD) is of widespread interest across many industries, including finance, healthcare, and manufacturing. Despite the development of numerous automatic methods for detecting anomalies, human oversight remains necessary to review and act upon detected anomalies, as well as verify their accuracy. We study the use of multimodal large language models (LLMs) to partially automate this process. We find that LLMs can effectively identify false alarms by integrating visual inspection of time series plots with text descriptions of the data-generating process. By leveraging the capabilities of LLMs, we aim to reduce the reliance on human effort required to maintain a TSAD system
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