Reversible Residual Normalization Alleviates Spatio-Temporal Distribution Shift

2026-04-17

AI Summary

This work addresses the significant performance degradation of spatiotemporal forecasting models on graph-structured data under spatiotemporal distribution shifts. To mitigate this issue, the authors propose the Reversible Residual Normalization (RRN) framework, which uniquely integrates invertible transformations with graph-aware normalization. RRN constructs a bidirectional invertible architecture by combining spectral-constrained graph neural networks, centering normalization, and reversible residual modules, enabling spatially aware adaptive correction while preserving invertibility. The framework is model-agnostic and allows recovery of original distributional characteristics from the normalized latent space. Extensive experiments demonstrate that RRN substantially enhances generalization and robustness across multiple spatiotemporal prediction tasks.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Graph-based Machine Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

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

Abstract

Distribution shift severely degrades the performance of deep forecasting models. While this issue is well-studied for individual time series, it remains a significant challenge in the spatio-temporal domain. Effective solutions like instance normalization and its variants can mitigate temporal shifts by standardizing statistics. However, distribution shift on a graph is far more complex, involving not only the drift of individual node series but also heterogeneity across the spatial network where different nodes exhibit distinct statistical properties. To tackle this problem, we propose Reversible Residual Normalization (RRN), a novel framework that performs spatially-aware invertible transformations to address distribution shift in both spatial and temporal dimensions. Our approach integrates graph convolutional operations within invertible residual blocks, enabling adaptive normalization that respects the underlying graph structure while maintaining reversibility. By combining Center Normalization with spectral-constrained graph neural networks, our method captures and normalizes complex Spatio-Temporal relationships in a data-driven manner. The bidirectional nature of our framework allows models to learn in a normalized latent space and recover original distributional properties through inverse transformation, offering a robust and model-agnostic solution for forecasting on dynamic spatio-temporal systems.

Problem

• distribution shift
• spatio-temporal forecasting
• graph heterogeneity
• temporal drift
• spatial network

Innovation

• Reversible Residual Normalization
• Spatio-Temporal Distribution Shift
• Graph Neural Networks
• Invertible Transformations
• Instance Normalization