Can LLMs Serve As Time Series Anomaly Detectors?

• URL: http://arxiv.org/abs/2408.03475v1
• Date: Tue, 6 Aug 2024 23:14:39 GMT
• Title: Can LLMs Serve As Time Series Anomaly Detectors?
• Authors: Manqing Dong, Hao Huang, Longbing Cao,
• Abstract summary: An emerging topic in large language models (LLMs) is their application to time series forecasting. In this paper, we investigate the capabilities of LLMs, specifically GPT-4 and LLaMA3, in detecting and explaining anomalies in time series.
• Score: 33.28502093260832
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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