I'm doing time series forecasting for a biological system. I haven't used these foundation models but my experience is that knowledge of how things are related is baked into what variables you put into the model (which can be more than raw data, you can make any derived product you think would be useful)
but it's up to the model to figure out how they are related (if I knew exactly how they were related I wouldn't be training a forecasting model).
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.
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.