Colour me old-fashioned, but i am really sceptical about a zero-shot general-purpose LLM-based anomaly detector being able to outperform a trivial, not-obviously-wrong ML/statistical model calibrated on a small subset of the data that makes training extremely fast, in entirely new domains. And in anomaly detection, false negatives (and even false positives) can be very expensive.

The most promising role for LLMs in timeseries prediction is in extracting covariates from unstructured data, in the wind example, weather reports and geographically proximal social media posts.