mlsanityData to 14 Oct 2026

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

arXiv 2508.16161 ↗First posted 22 Aug 2025cs.LG cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 38 of its 62 references that are in the map. First posted on arXiv 22 Aug 2025.

Builds on

38 of 62 references.

References · 38

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26 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025ARIES: Relation Assessment and Model Recommendation for Deep Time Series Forecastingcited by 1
  2. 2025APT: Affine Prototype-Timestamp For Time Series Forecasting Under Distribution Shiftcited by 1
  3. 2026Uniform Inductive Spatio-Temporal Krigingcited by 0
  4. 2026A Dataset and Model for Imputing Water Surface Elevation on a Large and Extremely Sparse Spatiotemporal Graphcited by 0