Accepted at NeurIPS 2026 TLDR: The paper presents a library of synthetic time series generators and augmentations to train time series foundation models, in their case a DeltaProduct based linear RNN. The synthetic data has already been used in Toto-2 and TS-ICL. Arxiv: https://arxiv.org/abs/2510.25502 GitHub: https://github.com/automl/TempoPFN Huggingface: https://huggingface.co/AutoML-org/TempoPFN Authors: Vladyslav Moroshan, Julien Siems, Arber Zela, Timur Carstensen, Frank Hutter   submitted by   /u/Yossarian_1234 [link]   [comments]