It's true simply because large ANNs have a higher capacity, which is great for large, nonlinear data but less so for small datasets or simple functions.

In any case, Transformers are eating ML right now and I'm actually surprised there's no "GPT-3 for time series" yet. It's technically the same problem as language modeling (that is, multi-step prediction of numerics), however, there is only a comparably little amount of human-generated data for self-supervised learning of a time series forecasting model. Another reason might be that the expected applications and potentials of such a pre-trained model aren't as glamorous as generating language.