Reversible Residual Normalization (RRN) introduces spatially-aware invertible residual blocks that combine center normalization with spectral-constrained graph convolutions to mitigate spatio-temporal distribution shifts in graph forecasting.
ST-Norm: Spatial and Temporal Normalization for Multi-Variate Time Series Forecasting
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Reversible Residual Normalization Alleviates Spatio-Temporal Distribution Shift
Reversible Residual Normalization (RRN) introduces spatially-aware invertible residual blocks that combine center normalization with spectral-constrained graph convolutions to mitigate spatio-temporal distribution shifts in graph forecasting.