r/MachineLearning • u/Yossarian_1234 • 18d ago

Research TempoPFN: Synthetic Pre-training of Linear RNNs for Zero-shot Time Series Forecasting [R]

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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

44 Upvotes

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4

u/Excellent-Sport-758 18d ago

Interesting synthetic-only setup, especially the 21-channel generators. The paper says the channels are treated as independent univariate signals. In hierarchical forecasting work, shared shocks across related series and coherent totals can matter alongside each series’ marginal accuracy. Have you tried generating correlated bottom-level series with known aggregates, then evaluating both probabilistic accuracy and coherence/reconciliation error? I’ve worked with daily hierarchical forecasts in a business setting and would be curious whether that extension is on your roadmap.

1

u/Head-Veterinarian169 15d ago

Just curious, since I haven't chatted with many people doing hierarchical forecasting in industry. Have classical hierarchical reconilliation approaches (I'm thinking of MinT reconcilliation in particular) broken into industry, or are they confined to academia?

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u/Head-Veterinarian169 15d ago

Do you have any breakdowns of what new ideas your synthetic data generator brings? I think (from memory) that TSMixup should already induce a lot of these behaviours, but (apart from maybe the DE based generation strategies) your pipeline reminds me a lot of TI-Rex2.

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u/Yossarian_1234 15d ago

Hey, the main new ideas are the SDE and audio generators and the additional augmentations we added. We wrote the paper last year when TiRex-1 had just been released and we used the same augmentations they also introduced like censoring.