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Caformer: Rethinking Time Series Analysis from Causal Perspective
The spurious correlation induced by the environment confounds the causal relationships between cross-dimension and cross-time dependencies.
SAMSGL: Series-Aligned Multi-Scale Graph Learning for Spatio-Temporal Forecasting
Despite the significant strides made by graph-based networks in spatio-temporal forecasting, there remain two pivotal factors closely related to forecasting performance that need further consideration: time delays in propagation dynamics and multi-scale high-dimensional interactions.
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