STA-GANN: A Valid and Generalizable Spatio-Temporal Kriging Approach

2025-08-22

AI Summary

Addressing challenges in spatiotemporal kriging—including difficulty modeling dynamic spatial dependencies under sensor missingness, sensitivity to temporal misalignment, and poor cross-sensor generalizability—this paper proposes a novel graph neural network framework. Methodologically, it introduces (1) a phase-decoupling module that explicitly models timestamp offsets; (2) a data-driven meta-graph construction mechanism to adaptively capture evolving spatial dependencies; and (3) an adversarial transfer learning strategy to enhance generalization to unseen sensors. Evaluated on nine real-world datasets across four domains, the method consistently outperforms state-of-the-art baselines in both imputation accuracy and cross-domain generalizability, achieving new SOTA performance. Theoretical analysis further substantiates its effectiveness and robustness under sensor sparsity and temporal heterogeneity.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Inferring missing spatio-temporal data from incomplete sensor observations
• Ensuring validity and generalizability of inferred spatio-temporal patterns
• Capturing dynamic spatial dependencies and temporal shifts effectively

Innovation

• Decoupled Phase Module adjusts timestamp shifts
• Dynamic Metadata Graph updates spatial relationships
• Adversarial transfer learning ensures generalizability