Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion
Wenying He, Jieling Huang, Junhua Gu, Ji Zhang, Yude Bai·June 08, 2025
Summary
CoFILL, a novel conditional diffusion model, tackles spatiotemporal data imputation challenges by capturing complex interdependencies between spatial and temporal dimensions, reducing cumulative errors. It integrates a dual-stream architecture for simultaneous processing of temporal and frequency domain features, outperforming state-of-the-art methods in accuracy. CoFILL's noise prediction network transforms random noise into meaningful values, addressing limitations of traditional approaches like RNN and GNN. The text also highlights advancements in neural information processing systems, focusing on techniques like attention mechanisms, adaptive spatial-temporal correlations, and multiple imputation, showcasing progress in handling incomplete data across various fields.
Introduction
Background
Explanation of spatiotemporal data challenges
Importance of accurate data imputation in various fields
Objective
Aim of CoFILL in addressing spatiotemporal data imputation challenges
Highlighting the model's capability in capturing complex interdependencies and reducing cumulative errors
Method
Dual-Stream Architecture
Description of the dual-stream design for simultaneous processing of temporal and frequency domain features
Explanation of how this architecture enhances the model's performance in handling spatiotemporal data
Noise Prediction Network
Detailed explanation of the noise prediction component
How it transforms random noise into meaningful values, improving upon traditional approaches like RNN and GNN
Integration of Advanced Techniques
Overview of techniques like attention mechanisms, adaptive spatial-temporal correlations, and multiple imputation
Discussion on how these advancements contribute to the model's effectiveness in neural information processing systems
Performance Evaluation
Comparison with State-of-the-Art Methods
Metrics used for performance evaluation
Detailed comparison showcasing CoFILL's superiority in accuracy
Case Studies
Illustrative examples demonstrating the model's application and effectiveness in real-world scenarios
Conclusion
Summary of Contributions
Recap of CoFILL's unique features and advancements
Future Directions
Potential areas for further research and development
Impact on Spatiotemporal Data Imputation
Discussion on the broader implications of CoFILL's contributions to the field
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
What are the key components of CoFILL's noise prediction network, and how does it transform random noise into meaningful values for data imputation?
How does CoFILL's dual-stream architecture process temporal and frequency domain features to improve spatiotemporal data imputation?
What are the limitations of traditional approaches like RNN and GNN that CoFILL addresses in spatiotemporal data imputation?
How does CoFILL capture complex interdependencies between spatial and temporal dimensions to reduce cumulative errors in data imputation?