<ul><li>This paper presents a method to improve the robustness of spatiotemporal long-term prediction using variational mode graph convolutional networks (VMGCN) with 3D channel attention.</li><li>The method incorporates i.i.d. Gaussian noise to a large traffic volume dataset and applies variational mode decomposition to model the corrupted signal.</li><li>A 3D attention mechanism is integrated to learn spatial, temporal, and channel correlations and to suppress noise while highlighting significant modes in the spatiotemporal signals.</li><li>The proposed method outperforms baseline models in terms of long-term prediction accuracy, robustness to noise, and improved performance with mode truncation.</li></ul>

Robust and Noise-resilient Long-Term Prediction of Spatiotemporal Data Using Variational Mode Graph Neural Networks with 3D Attention

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