Problem is you really don't even need ML for the curve fitting tools. Just ARIMA or Holt Winters is enough and likely faster. Heck, Kubernetes HPA doesn't even implement full PID, just P.
It seems like the main challenge is going to be finding domains where accuracy is important enough to spend inference token money on, but not so important that you'd hire a team to build a custom model. Or maybe that team borrows Timestral weights to fine tune to the domain, but they probably already have a different model customized to the problem?
You're right but, if the time series looks like music then you can apply your music domain knowledge. You don't have to only apply knowledge that's general across every domain, part of the magic of large neural nets is that you can do recognition and prediction at the same time thereby using (statistically) things known from training about how to identify a sequence and then also how to continue it.
Sure adding outside info helps even more, but with enough data it should become unnecessary (or so goes the theory).
Is there a reason you don't throw in something like cross-attention on an embedding of the sequence description/data labels? I've trained numerous generative models for audio and images and it's always the case that the unconditional model performs worse, trains slower, etc. than the conditional one (with the best performance from a hybrid using classifier-free guidance or similar scheme).
Again I don't have experience with the domain of time-series forecasting directly, but I would think there are generalities between autoregressive generative models and time-series forecasting, given that both involve predicting the next sample(s) given N regularly sampled past inputs.
Won't you need to attribute a weight to the data coming from the model and the weight for your data?
Would love to know actual time series foundation model use cases!
I'm sure someone who actually knows this stuff has a better easy to view it but my impression was that these time series foundation models aren't trained for a specific dataset but more they are trained on a procedure for how to look at datasets and make predictions. It doesn't seem to lump things into a bucket much more than any other model type (which also are just consistent procedures to look at datasets). It's not like you have radically different model types across different fields.
That seems like a scenario where there wouldn't be external sources of information capable of improving your predictions. It also seemed to me like a scenario where good old fashioned statistical methods would outperform LSTMs, which was what they used.
(I didn't get the job - I was offered it, but they rescinded their offer at the last moment, supposedly because their investors had insisted they should hire a web developer instead.)
To improve the robustness of machine learning models, artificial noise is often introduced into actual training data. Something like Timestral could simplify the process of generating it.