Abstract
Spatio-temporal tasks often encounter incomplete data arising from missing or inaccessible sensors, making spatio-temporal kriging crucial for inferring the completely missing temporal information. However, current models struggle with ensuring the validity and generalizability of inferred spatio-temporal patterns, especially in capturing dynamic spatial dependencies and temporal shifts, and optimizing the generalizability of unknown sensors. To overcome these limitations, we propose Spatio-Temporal Aware Graph Adversarial Neural Network (STA-GANN), a novel GNN-based kriging framework that improves spatio-temporal pattern validity and generalization. STA-GANN integrates (i) Decoupled Phase Module that senses and adjusts for timestamp shifts. (ii) Dynamic Data-Driven Metadata Graph Modeling to update spatial relationships using temporal data and metadata; (iii) An adversarial transfer learning strategy to ensure generalizability. Extensive validation across nine datasets from four fields and theoretical evidence both demonstrate the superior performance of STA-GANN.
Abstract (translated)
空间时间任务常常遇到由于传感器缺失或无法访问而导致的不完整数据，使得时空克里金法（一种用于推断完全丢失的时间信息的技术）变得至关重要。然而，目前的模型在确保所推测的空间时间模式的有效性和泛化能力方面存在困难，尤其是在捕捉动态空间依赖关系和时间变化以及优化未知传感器的泛化性方面。为了解决这些限制，我们提出了一种基于图神经网络（GNN）的新框架——时空感知图对抗神经网络（STA-GANN），该框架能够改进时空模式的有效性和泛化能力。 STA-GANN整合了以下三个关键部分： (i) 解耦相位模块：可以检测并调整时间戳的偏移； (ii) 动态数据驱动元数据图建模：利用时间数据和元数据来更新空间关系； (iii) 对抗性迁移学习策略，以确保模型具有良好的泛化能力。 通过在四个领域的九个不同数据集上的广泛验证以及理论证据都证明了STA-GANN的优越性能。
URL
https://arxiv.org/abs/2508.16161
