STFO 提出将时空预测建模为场演化算子，应对传感器网络扩张
More Sensors Only One Field: Rethinking Continual Spatio-Temporal Forecasting
遇到传感器越加越多、老模型就失效的问题？这篇论文用场算子代替图表示，PEMS 上误差直接降了 8.4%，代码也开源了。
arXiv 论文提出 STFO（Spatio-Temporal Field Operator），解决持续时空预测中传感器扩张导致图表示失效的问题。
该方法将预测知识参数化为共享场演化算子，通过坐标归一化聚合把不规则传感器历史映射到固定潜网格，无需传感器专属参数。针对过程漂移，模型用谱描述子刻画不同空间尺度的变化，以调节 Fourier 传播和注意力机制。
在 PEMS-Stream、CA-Stream 和 AIR-Stream 三个基准上，STFO-Large 的平均 MAE 比 DOL 降低 8.4%（PEMS-Stream）和 4.
7%（CA-Stream）。
More Sensors Only One Field: Rethinking Continual Spatio-Temporal Forecasting
Continual spatio-temporal forecasting supports traffic management and environmental monitoring under evolving dynamics and expanding sensor networks. However, conventional graph-based continual learning methods tie forecasting representations to the current sensor layout, so sensor expansion can alter the representation of learned spatial relationships. Our key insight is that sensor expansion changes the evidence available about a process without necessarily changing the dynamics to be learned. We propose STFO (Spatio-Temporal Field Operator), which parameterizes forecasting knowledge as a shared field-evolution operator and handles changing sensor layouts through observation and query interfaces. Normalized coordinate-based aggregation lifts irregular sensor histories onto a fixed latent grid, enabling reuse of learned spatial maps across observation sets without sensor-specific parameters. To accommodate process drift, a spectral descriptor summarizes variation across spatial scales and conditions Fourier propagation and attention to adapt operator responses to the current spatial regime. Coordinate-based decoding queries the evolved field at sensor locations and combines spatial corrections with local-history predictions. Experiments on PEMS-Stream, CA-Stream, and AIR-Stream demonstrate state-of-the-art average forecasting performance. STFO-Large reduces average MAE over DOL by 8.4% on PEMS-Stream and 4.7% on CA-Stream. Our code is available at https://github.com/Xielewei/Spatio-Temporal-Field-Operator.
本条情报归属 AITOP「论文」核心追踪栏目。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：STFO、时空预测、持续学习、开源模型，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「arXiv cs.LG」（@Lewei Xie, Haoyu Zhang, Jiajun Zhou, Yulong Chen, Guanxing Chen, Yu-An Huang, Hau-San Wong, Yifan Zhang, Zhi-An Huang），经 AITOP 多维评分管线结构化提炼。
