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Robust Spatiotemporal Forecasting Using Adaptive Deep-Unfolded Variational Mode Decomposition

arXiv 2509.00703 ↗First posted 31 Aug 2025cs.LGcited by 0

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Abstract (arXiv)

Accurate spatiotemporal forecasting is critical for numerous complex systems but remains challenging due to complex volatility patterns and spectral entanglement in conventional graph neural networks (GNNs). While decomposition-integrated approaches like variational mode graph convolutional network (VMGCN) improve accuracy through signal decomposition, they suffer from computational inefficiency and manual hyperparameter tuning. To address these limitations, we propose the mode adaptive graph network (MAGN) that transforms iterative variational mode decomposition (VMD) into a trainable neural module. Our key innovations include (1) an unfolded VMD (UVMD) module that replaces iterative optimization with a fixed-depth network to reduce the decomposition time (by 250x for the LargeST benchmark), and (2) mode-specific learnable bandwidth constraints (αk ) adapt spatial heterogeneity and eliminate manual tuning while preventing spectral overlap. Evaluated on the LargeST benchmark (6,902 sensors, 241M observations), MAGN achieves an 85-95% reduction in the prediction error over VMGCN and outperforms state-of-the-art baselines.

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References · 15

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  3. 2019Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processingcited by 421
  4. 2022Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecastingcited by 123
  5. 2022Deep Generalized Unfolding Networks for Image Restorationcited by 121
  6. 2023LargeST: A Benchmark Dataset for Large-Scale Traffic Forecastingcited by 118
  7. 2014Deep Unfolding: Model-Based Inspiration of Novel Deep Architecturescited by 77
  8. 2020Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocationcited by 61
  9. 2018Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasoundcited by 44
  10. 2024Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspectivecited by 26
  11. 2024RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Datacited by 17
  12. 2023Urban Regional Function Guided Traffic Flow Predictioncited by 12
3 more
  1. 2024Variational Mode-Driven Graph Convolutional Network for Spatiotemporal Traffic Forecastingcited by 2
  2. 2025Robust and Noise-resilient Long-Term Prediction of Spatiotemporal Data Using Variational Mode Graph Neural Networks with 3D Attentioncited by 1
  3. 2025Efficient Traffic Forecasting on Large-Scale Road Network by Regularized Adaptive Graph Convolutioncited by 1

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