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marketed as a forecasting tool, so is this not applicable to event classification in time series?

I'd say that's kind of a different task. I'm not a pro in this, but you could maybe treat it as a multi-variate forecast problem where the targets are probabilities per event if n is really small?

Yes, I would be interested where this (and any Transformer/LLM based approach) is improving anomaly detection for example.

I can't speak for all use cases, but I've done a great deal of work in the space of using deep learning approaches for anomaly detection in network device telemetry. In particular with high resolution univariate time series of latency measurements, we saw success using convolutional autoencoders and GANs. These methods lean on reconstruction loss rather than forecasting, but still effective.

There is some prior art for this that we leaned on [1][2].

RE: transformers — I did some early experimentation with Temporal Fusion Transformers [3] which worked pretty well for forecasting compared to other deep learning methods, but rarely did I see it outperform standard baselines (like ARIMA) in our datasets.

[1] https://www.mdpi.com/2076-3417/12/23/12472

[2] https://arxiv.org/abs/2009.07769

[3] https://arxiv.org/abs/1912.09363

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