Reversible Residual Normalization Alleviates Spatio-Temporal Distribution Shift

• URL: http://arxiv.org/abs/2604.15838v1
• Date: Fri, 17 Apr 2026 08:40:28 GMT
• Title: Reversible Residual Normalization Alleviates Spatio-Temporal Distribution Shift
• Abstract summary: We propose Reversible Residual Normalization (NRR), a framework that performs spatially-aware invertible normalization to address distribution shift in both spatial and temporal dimensions.<n>Our approach integrates graph convolutional operations within invertible residual blocks, enabling adaptive normalization that respects the underlying graph structure while maintaining reversibility.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Incremental Aggregation on the Grassmannian for Asynchronous Eigenspace Computation [37.26483427088676]
  We study asynchronous optimization for finite-sum eigenspace computation in heterogeneous distributed systems.<n>We propose a Grassmannian incremental aggregation method that refreshes only arriving components and reuses cached gradients.<n> Experiments on serial and distributed PCA demonstrate improved sample efficiency and wall-clock convergence over representative baselines.
  arXiv  Detail & Related papers  (2026-08-05T03:21:38Z)
• Parallel Complex Diffusion for Scalable Time Series Generation [50.01609741902786]
  PaCoDi is a spectral-native architecture that decouples generative modeling in the frequency domain.<n>We show that PaCoDi outperforms existing baselines in both generation quality and inference speed.
  arXiv  Detail & Related papers  (2026-02-10T14:31:53Z)
• An Elementary Approach to Scheduling in Generative Diffusion Models [55.171367482496755]
  An elementary approach to characterizing the impact of noise scheduling and time discretization in generative diffusion models is developed.<n> Experiments across different datasets and pretrained models demonstrate that the time discretization strategy selected by our approach consistently outperforms baseline and search-based strategies.
  arXiv  Detail & Related papers  (2026-01-20T05:06:26Z)
• Deep Delta Learning [91.75868893250662]
  We introduce Deep Delta Learning (DDL), a novel architecture that generalizes the standard residual connection.<n>We provide a spectral analysis of this operator, demonstrating that the gate $(mathbfX)$ enables dynamic between identity mapping, projection, and geometric reflection.<n>This unification empowers the network to explicitly control the spectrum of its layer-wise transition operator, enabling the modeling of complex, non-monotonic dynamics.
  arXiv  Detail & Related papers  (2026-01-01T18:11:38Z)
• Simulating Distribution Dynamics: Liquid Temporal Feature Evolution for Single-Domain Generalized Object Detection [58.25418970608328]
  Single-Domain Generalized Object Detection (Single-DGOD) aims to transfer a detector trained on one source domain to multiple unknown domains.<n>Existing methods for Single-DGOD typically rely on discrete data augmentation or static perturbation methods to expand data diversity.<n>We propose a new method, which simulates the progressive evolution of features from the source domain to simulated latent distributions.
  arXiv  Detail & Related papers  (2025-11-13T03:10:39Z)
• IBN: An Interpretable Bidirectional-Modeling Network for Multivariate Time Series Forecasting with Variable Missing [6.481926629151858]
  We propose the Interpretable Bidirectional-modeling Network (IBN)<n>IBN integrates Uncertainty-Aware Interpolation (UAI) and Graph Convolution (GGCN)<n>Experiments show that IBN achieves state-of-the-art forecasting performance under various missing-rate scenarios.
  arXiv  Detail & Related papers  (2025-09-09T13:27:21Z)
• Generalized Linear Mode Connectivity for Transformers [87.32299363530996]
  A striking phenomenon is linear mode connectivity (LMC), where independently trained models can be connected by low- or zero-loss paths.<n>Prior work has predominantly focused on neuron re-ordering through permutations, but such approaches are limited in scope.<n>We introduce a unified framework that captures four symmetry classes: permutations, semi-permutations, transformations, and general invertible maps.<n>This generalization enables, for the first time, the discovery of low- and zero-barrier linear paths between independently trained Vision Transformers and GPT-2 models.
  arXiv  Detail & Related papers  (2025-06-28T01:46:36Z)
• Multivariate Long-term Time Series Forecasting with Fourier Neural Filter [42.60778405812048]
  We introduce FNF as the backbone and DBD as architecture to provide excellent learning capabilities and optimal learning pathways for spatial-temporal modeling.<n>We show that FNF unifies local time-domain and global frequency-domain information processing within a single backbone that extends naturally to spatial modeling.
  arXiv  Detail & Related papers  (2025-06-10T18:40:20Z)
• DRAN: A Distribution and Relation Adaptive Network for Spatio-temporal Forecasting [19.064628208136273]
  We propose a Distribution Relation and Adaptive Network (DRAN) capable of dynamically adapting to and distribution changes over time.<n>A Spatial Factor Learner (SFL) module is developed that enables the normalization de-normalization process.<n>Our approach outperforms state-of-the-art methods on weather prediction and traffic flow forecasting tasks.
  arXiv  Detail & Related papers  (2025-04-02T09:18:43Z)
• How to Bridge Spatial and Temporal Heterogeneity in Link Prediction? A Contrastive Method [11.719027225797037]
  We propose a novel textbfContrastive Learning-based textbfLink textbfPrediction model, textbfCLP.<n>Our mymodel consistently outperforms the state-of-the-art models, demonstrating an average improvement of 10.10%, 13.44% in terms of AUC and AP.
  arXiv  Detail & Related papers  (2024-11-01T14:20:53Z)
• GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing [21.980379175333443]
  We propose a novel Graph Interpolation Attention Recursive Network (named GinAR) to model the spatial-temporal dependencies over the limited collected data for forecasting. In GinAR, it consists of two key components, that is, attention and adaptive graph convolution. Experiments conducted on five real-world datasets demonstrate that GinAR outperforms 11 SOTA baselines, and even when 90% of variables are missing, it can still accurately predict the future values of all variables.
  arXiv  Detail & Related papers  (2024-05-18T16:42:44Z)
• Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality [39.476378833827184]
  Anomaly detection in dynamic graphs presents a significant challenge due to the temporal evolution of graph structures and attributes. We introduce a novel spatial- temporal memories-enhanced graph autoencoder (STRIPE) STRIPE significantly outperforms existing methods with 5.8% improvement in AUC scores and 4.62X faster in training time.
  arXiv  Detail & Related papers  (2024-03-14T02:26:10Z)
• Generative Model without Prior Distribution Matching [26.91643368299913]
  Variational Autoencoder (VAE) and its variations are classic generative models by learning a low-dimensional latent representation to satisfy some prior distribution. We propose to let the prior match the embedding distribution rather than imposing the latent variables to fit the prior.
  arXiv  Detail & Related papers  (2020-09-23T09:33:24Z)
• Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation [72.40827239394565]
  We propose to compute features only at sparsely sampled locations. We then densely reconstruct the feature map with an efficient procedure. The presented network is experimentally shown to save substantial computation while maintaining accuracy over a variety of computer vision tasks.
  arXiv  Detail & Related papers  (2020-03-19T15:36:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.