  Recent studies have shown the ability of large language models to perform a
variety of tasks, including time series forecasting. The flexible nature of
these models allows them to be used for many applications. In this paper, we
present a novel study of large language models used for the challenging task of
time series anomaly detection. This problem entails two aspects novel for LLMs:
the need for the model to identify part of the input sequence (or multiple
parts) as anomalous; and the need for it to work with time series data rather
than the traditional text input. We introduce sigllm, a framework for time
series anomaly detection using large language models. Our framework includes a
time-series-to-text conversion module, as well as end-to-end pipelines that
prompt language models to perform time series anomaly detection. We investigate
two paradigms for testing the abilities of large language models to perform the
detection task. First, we present a prompt-based detection method that directly
asks a language model to indicate which elements of the input are anomalies.
Second, we leverage the forecasting capability of a large language model to
guide the anomaly detection process. We evaluated our framework on 11 datasets
spanning various sources and 10 pipelines. We show that the forecasting method
significantly outperformed the prompting method in all 11 datasets with respect
to the F1 score. Moreover, while large language models are capable of finding
anomalies, state-of-the-art deep learning models are still superior in
performance, achieving results 30% better than large language models.
