Reversible Residual Normalization Alleviates Spatio-Temporal Distribution Shift
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 tra...
| Authors | Zhaobo Hu, Vincent Gauthier, Mehdi Naima |
|---|---|
| Primary category | cs.LG |
| Published | 17 April 2026 |
| Last updated at source | 18 June 2026 |
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