An emerging topic in large language models (LLMs) is their application to time series forecasting, characterizing mainstream and patternable characteristics of time series. A relevant but rarely explored and more challenging question is whether LLMs can detect and explain time series anomalies, a critical task across various real-world applications. In this paper, we investigate the capabilities of LLMs, specifically GPT-4 and LLaMA3, in detecting and explaining anomalies in time series. Our studies reveal that: 1) LLMs cannot be directly used for time series anomaly detection. 2) By designing prompt strategies such as in-context learning and chain-of-thought prompting, GPT-4 can detect time series anomalies with results competitive to baseline methods. 3) We propose a synthesized dataset to automatically generate time series anomalies with corresponding explanations. By applying instruction fine-tuning on this dataset, LLaMA3 demonstrates improved performance in time series anomaly detection tasks. In summary, our exploration shows the promising potential of LLMs as time series anomaly detectors.

本研究解决了大型语言模型（LLMs）在时间序列异常检测方面的不足，探讨了LLMs是否能检测和解释时间序列异常。在设计了上下文学习和思维链提示策略后，GPT-4能够有效识别异常，并与基线方法竞争。此外，我们提出了一种合成数据集来自动生成时间序列异常及其解释，并通过指令微调提升了LLaMA3在异常检测任务中的表现。研究表明，LLMs在时间序列异常检测方面具有良好的潜力。

大型语言模型能否作为时间序列异常检测器？