TempoPFN: zero-shot time series forecasting via synthetic pre-training of linear RNNs, NeurIPS accepted
Yossarian_1234 · reddit · 2026-10-19
- Paper: TempoPFN (accepted at NeurIPS, Frank Hutter's group) introduces a library of synthetic time series generators and augmentations used to pre-train a DeltaProduct-based linear RNN foundation model for zero-shot forecasting.
- Data: generators plus an augmentation chain (TS Mix, noise, quantization, scaling, convolution, censoring, NaN injection) — no real time series needed.
- Adoption: the synthetic data is already used in Toto-2 and TS-ICL.
- Evaluation: competitive against TiRex, TabPFN-TS and Chronos Bolt on GIFT-Eval while trained purely on synthetic data.
- Resources: arXiv 2510.25502; open-sourced on GitHub (automl/TempoPFN) and Hugging Face (AutoML-org/TempoPFN).
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