TempoPFN：用合成预训练让线性 RNN 实现零样本时间序列预测

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

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被 NeurIPS 2026 接收的论文 TempoPFN 提出一套合成时间序列生成器与增强方法，用于训练时间序列基础模型，其模型为线性 RNN。作者称这些合成数据已被 Toto-2 和 TS-ICL 使用，论文、GitHub 代码与 Hugging Face 模型均已公开。

正文

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

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 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/TempoPF N

Authors: Vladyslav Moroshan, Julien Siems, Arber Zela, Timur Carstensen, Frank Hutter

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