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

Yujie Li, Zezhi Shao, Chengqing Yu, Tangwen Qian, Zhao Zhang, Yifan Du, Shaoming He, Fei Wang, Yongjun Xu

TL;DR

This work tackles the difficulty of inferring complete spatio-temporal patterns when sensors are missing or inaccessible. It introduces STA-GANN, a spatio-temporal kriging framework that combines Dynamic Data-Driven Metadata Graph Modeling (D3MGM) to capture dynamic spatial relations, a Decoupled Phase Module (DPM) to align timestamp shifts in the frequency domain, and adversarial transfer learning to improve generalization to unseen sensors. The authors provide theoretical grounding based on domain adaptation and demonstrate consistent, substantial improvements across nine real-world datasets over strong baselines, along with comprehensive ablations and analyses of generalization, missing data scenarios, and runtime. The approach offers a practical pathway to more reliable and scalable spatio-temporal kriging in sensor networks, with potential impact on energy, transportation, meteorology, and environmental monitoring applications.

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.

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

TL;DR

This work tackles the difficulty of inferring complete spatio-temporal patterns when sensors are missing or inaccessible. It introduces STA-GANN, a spatio-temporal kriging framework that combines Dynamic Data-Driven Metadata Graph Modeling (D3MGM) to capture dynamic spatial relations, a Decoupled Phase Module (DPM) to align timestamp shifts in the frequency domain, and adversarial transfer learning to improve generalization to unseen sensors. The authors provide theoretical grounding based on domain adaptation and demonstrate consistent, substantial improvements across nine real-world datasets over strong baselines, along with comprehensive ablations and analyses of generalization, missing data scenarios, and runtime. The approach offers a practical pathway to more reliable and scalable spatio-temporal kriging in sensor networks, with potential impact on energy, transportation, meteorology, and environmental monitoring applications.

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.

Paper Structure

This paper contains 23 sections, 17 equations, 9 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Graph Neural Network
  4. Spatio-Temporal Forecasting
  5. Spatio-Temporal Imputation & Kriging
  6. Transfer Learning
  7. Proposed Method
  8. Overview
  9. Dynamic Data-Driven Metadata Graph Modeling
  10. Decoupled Phase Module
  11. Adversarial Transfer Learning Strategy
  12. Experiments
  13. Datasets
  14. Main Results
  15. Ablation Study
  16. ...and 8 more sections

Figures (9)

  • Figure 1: Challenges of Spatio-Temporal Kriging. (A) Predefined graphs may contain unpredictable errors because some edge relations are incorrectly established or ignored. (B) Timestamp shift refers to delays in the transmission of temporal information caused by inevitable factors such as distance and upstream-downstream relationships. (C) The captured overfitted patterns may only be suitable for specific sensors and cannot be generalized.
  • Figure 2: Architecture of STA-GANN
  • Figure 3: Phase Shift Learning
  • Figure 4: Confusion between known and unknown sensors on PEMS03 and PEMS08
  • Figure 5: Experimental results for the missing rate
  • ...and 4 more figures