  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.
