Sadly the paper uses for benchmarks datasets that: - are known to be pretty useless - contain mistakes - can be misleading with a naive F1-score measure. (to be fair they write "we looked at the F1-Score, under which both partial and full anomaly detection are considered correct identification" so this may be mitigated, but it's not clear)

See https://kdd-milets.github.io/milets2021/slides/Irrational%20... So it's hard to take any benchmark from the paper seriously. The paper is also ignoring any recent work (like > 2018) on univariate timeseries anomaly detection in the matrix profile space (eg MADRID).

The "practicality of usage" and conclusion sections are pretty correct though: it's expensive, slow, and no-shot is worthless if some other methods can train and infer in orders of magnitude less time.

It would have been interesting to see how the DETECTOR method performs when the LLM forecasting is replaced with some standard forecasting. (eg some auto ETS, if possible robust to anomalies in the training data). It looks like the natural follow up of this article is to remove the LLM altogether.