Can LLMs Serve As Time Series Anomaly Detectors?
<|reference_start|>Can LLMs Serve As Time Series Anomaly Detectors?: 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.<|reference_end|>
@article{dong2024can,
    title={Can LLMs Serve As Time Series Anomaly Detectors?},
    author={Manqing Dong, Hao Huang, Longbing Cao},
    journal={arXiv preprint arXiv:2408.03475},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03475},
    primaryClass={cs.LG cs.AI}
}