大语言模型能否充当时间序列异常检测器？
Can LLMs Serve As Time Series Anomaly Detectors?
AI总结：
本文研究GPT-4和LLaMA3能否检测并解释时间序列异常，发现直接使用不可行，但通过提示策略和合成数据集指令微调可显著提升其异常检测性能，展示出LLMs作为时间序列异常检测器的潜力。
摘要：
大语言模型（LLMs）中一个新兴的研究课题是将其应用于时间序列预测，刻画时间序列的主流特征和可模式化特性。一个相关但鲜有探索且更具挑战性的问题是：LLMs能否检测并解释时间序列异常，这是众多现实应用中的关键任务。本文研究了LLMs，特别是GPT-4和LLaMA3，在检测和解释时间序列异常方面的能力。我们的研究表明：1）LLMs不能直接用于时间序列异常检测。2）通过设计上下文学习和思维链提示等提示策略，GPT-4能够检测时间序列异常，其结果可与基线方法相媲美。3）我们提出了一个合成数据集，可自动生成带有相应解释的时间序列异常。通过在该数据集上应用指令微调，LLaMA3在时间序列异常检测任务中表现出更好的性能。总之，我们的探索表明LLMs作为时间序列异常检测器具有广阔潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
