GAS-PFN：图感知时空先验数据拟合网络实现零样本交通预测
GAS-PFN: Graph-Aware Spatiotemporal Prior-Data Fitted Networks for Zero-Shot Traffic Forecasting
AI 导读
研究者提出图感知时空先验数据拟合网络 GAS-PFN，将路网拓扑通过随机游走扩散编码进合成任务先验，实现零样本交通流量预测。其推理架构用两次 mask 条件化前向传播通过共享 Transformer 主干，分别建模单传感器时序动态与邻近传感器时空交互。
正文 · 原文
Abstract:Traffic forecasting requires modeling complex temporal dynamics and the spatial dependencies imposed by road topology. Supervised graph-based models achieve strong performance, but are often trained for a particular dataset and sensor graph, making deployment to new or evolving networks costly. Prior-Data Fitted Networks (PFNs) offer a zero-shot alternative by learning in-context inference from synthetic task priors, which provide an explicit mechanism for encoding domain-specific assumptions. Existing forecasting PFNs, however, do not explicitly represent the graph-structured dependencies governing traffic dynamics. To combine graph-aware traffic modeling with zero-shot adaptation, we introduce the Graph-Aware Spatiotemporal Prior-Data Fitted Network (GAS-PFN). GAS-PFN incorporates sampled road topology into its synthetic task prior through random-walk diffusion, teaching the model to learn graph-coupled dynamics during pretraining. Its co-designed inference architecture uses two mask-conditioned passes through a shared Transformer backbone to separately model sensor-specific temporal dynamics and spatiotemporal interactions among neighboring sensors before integrating their representations for prediction. This design enables GAS-PFN to use target-network structure and context without parameter updates. Across six benchmarks at the 90-minute horizon, GAS-PFN reduces MSE by 34.0% on average relative to TimePFN, its closest PFN-based predecessor. It also reduces MSE by 27.7% on average relative to four recent industry-developed general-purpose zero-shot forecasters. In this comparison, all models receive 90 minutes of contiguous recent history, and any additional context is drawn from each dataset's chronological training split. These results indicate that aligning PFN priors and inference architectures with graph structure improves in-context learning for zero-shot traffic forecasting.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE) |
| Cite as: | arXiv:2610.37598 [cs.LG] |
| (or arXiv:2610.37598v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.37598 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Joseph Natter [view email]
[v1]
Tue, 27 Oct 2026 17:31:26 UTC (1,667 KB)
来源：arXiv cs.AI · arxiv.org