TimesFM proposes a different deal: skip the training step entirely.
It is a pretrained model from Google Research that forecasts time series it has never seen before. Same idea as an LLM, except instead of predicting the next word, it predicts the next value. You hand it a NumPy array of history, tell it how far ahead to look, and get a forecast back. No training loop, no hyperparameter search.
This post covers what it actually does, how the API feels, what landed in the brand-new 3.0 release.
The mental model: patches are tokens
An LLM chops text into tokens and learns to predict the next one. TimesFM chops a time series into patches (contiguous windows of 32 time steps) and learns to predict the next patch.
