I feel the same way for the most part. However I cam imagine a kind of meta-learning foundation model that basically has a huge internal library of micro-featu

I feel the same way for the most part. However I cam imagine a kind of meta-learning foundation model that basically has a huge internal library of micro-features, and when you put a sequence into it, it matches those features against the sequence and builds up a low-noise summary of the data that it can use to make predictions. That's of course heavily anthropomorphized, but it seems potentially in-scope for a transformer model. The real problem with time series data is that you can't predict the future. Images and text are relatively homogeneous and exist within a kind of restricted space. "Time series" in general however could be just about anything, and there's not as much reason to believe that something like a "grammar of time series" even exists beyond what we already can do with STL etc.