用表格模型做时间序列预测，小模型也能达到顶尖水平。
From Tables to Time: Extending TabPFN-v2 to Time Series Forecasting
- 将时间序列转为表格回归问题，结合轻量时序特征与预训练模型。
- 1100万参数小模型在双基准上表现领先，覆盖单变量与多变量预测。
- 无需时序专有预训练，适合快速部署与跨领域应用。
近期基础模型进展使时间序列预测实现了强零样本性能。本文表明，此类能力也可从表格基础模型中涌现。我们提出TabPFN-TS，通过轻量时序特征化，将预测任务转化为表格回归问题，并使用预训练的TabPFN-v2。该方法无需时序特定预训练，天然支持单变量和协变量引导的预测。尽管模型仅1100万参数，但在GIFT-Eval与fev-bench基准上，协变量引导预测表现达最先进水平，单变量预测也具竞争力。我们进一步开展受控分析，考察模型如何理解时序结构、特征化选择对精度的影响，以及不同表格骨干网络下的预测变化。结果表明，搭配合适时序特征的表格基础模型，为预测提供了高效且灵活的统一框架。代码已公开于https://github.com/PriorLabs/tabpfn-time-series。
原文摘要 · Abstract (English)
Recent progress in foundation models has enabled strong zero-shot performance for time series forecasting. In this work, we show that such capabilities can also emerge from tabular foundation models. We introduce TabPFN-TS, a simple method that treats forecasting as a tabular regression problem by combining lightweight temporal featurization with the pretrained TabPFN-v2. This formulation requires no time-series-specific pretraining and naturally supports both univariate and covariate-informed forecasting. Despite its compact size (11M parameters), TabPFN-TS achieves state-of-the-art performance on covariate-informed forecasting and competitive accuracy on univariate forecasting across the GIFT-Eval and fev-bench benchmarks. We further provide controlled analyses examining how the model interprets temporal structure, how featurization choices affect accuracy, and how forecasts change under alternative tabular backbones. Together, our results demonstrate that tabular foundation models--when paired with suitable temporal features--offer an efficient and versatile alternative for forecasting, bridging tabular and time-series learning within a unified framework. Code is available at https://github.com/PriorLabs/tabpfn-time-series.
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