[Fripplebubby]

I think that the concept of a "foundation model" for time series is actually a bit flawed as presented in this blog post. A foundation model is interesting because it is capable of many tasks _beyond the target tasks_ that it was trained to do, whereas what the author is looking for is a time-series model that can make out-of-distribution predictions without re-training - which is, in my opinion, a problem that is pretty well solved by existing ARIMA and (especially) Prophet models (Yes, you have to re-fit the model on your distribution, but this is not at all akin to the task of training or fine-tuning an LLM, it's something you can do in seconds on a modern CPU, and yes, there are certain hyperparameters that may need to be selected, but they are actually fairly minimal).

But for a model to make out-of-distribution predictions does not make it a foundation model for time series, really that's just the basic task that all time series forecasting models do. A more interesting question is, does an LLM architecture seem to improve the task of univariate or multivariate time-series prediction? I don't think the answer is yes, although, depending on your domain, being able to use language inputs to your model may have a positive impact, and the best way to incorporate language inputs is certainly to use a transformer architecture, but that isn't what is addressed in this post.

[th0ma5]

A lot of people try to hedge this kind of sober insight along with their personal economic goals to say all manner of unfalsifiable statements of adequate application in some context, but it is refreshing to try to deal with the issues separately and I think a lot of people miss the insufficiency compared to traditional methods in all cases that I've heard of so far.

[cyanydeez]

Ai slop