Very cool that the dataset and model weights are open right away! This paper also doesn't have a bunch of weird architectural choices pulled out of nowhere like

Very cool that the dataset and model weights are open right away! This paper also doesn't have a bunch of weird architectural choices pulled out of nowhere like the other TS foundation models recently. Looks like it will actually be useful, thank you! Maybe I will actually get to do representation learning for TS during my PhD. As a sidenote/rant, it would be nice if all supervised TS benchmarks included "DLinear + RevIN" as the standard baseline, as in my experiments it tends to get about the same performance as all other new SOTA forecasting models. Most papers compare to the linear model without RevIN while they themselves use it, and only beat it because of that :) And in any case supervised training of transformers from scratch on datasets having less than 1M points is just stupid (so less raw data than a single image?). Less than 1B is still at least mildly stupid. Here of course the angle is zero-shot so its somewhat excused from this, but it still would be interesting whether it can beat that supervised model combination.