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Proposes a node-level spectral energy formulation for detecting camouflaged anomalies in graphs, extending to spatio-temporal settings with energy-driven message passing. Demonstrates effectiveness on large-scale benchmarks.
This paper proposes using the Ensemble Score Filter (EnSF), a score-based diffusion data assimilation method, to correct forecasts from a pretrained spatio-temporal energy consumption model using noisy partial observations. Numerical experiments show EnSF significantly improves state estimation over open-loop propagation and outperforms the Ensemble Kalman Filter under nonlinear observations.
Proposes a Global-Local Graph Attention Network (GLGAT) with pairwise encoding and event-based adjacency matrix for traffic forecasting, effectively capturing spatio-temporal correlations and achieving competitive performance on real-world datasets.