SigLLM

In a new study, MIT researchers found that large language models (LLMs) hold the potential to be more efficient anomaly detectors for time-series data. 

Importantly, these pretrained models can be deployed right out of the box.

The researchers developed a framework, called SigLLM, which includes a component that converts time-series data into text-based inputs an LLM can process. A user can feed these prepared data to the model and ask it to start identifying anomalies. The LLM can also be used to forecast future time-series data points as part of an anomaly detection pipeline.

While LLMs could not beat state-of-the-art deep learning models at anomaly detection, they did perform as well as some other AI approaches. 

If researchers can improve the performance of LLMs, this framework could help technicians flag potential problems in equipment like heavy machinery or satellites before they occur, without the need to train an expensive deep-learning model.