The content discusses the challenges of OOD traffic forecasting due to spatio-temporal shifts and proposes a novel approach, STEVE, to address these issues. The model incorporates causal inference theory and self-supervised tasks for improved performance across various scenarios.
The authors highlight the importance of removing spurious correlations caused by external factors in traffic data forecasting. They introduce a theoretical scheme called Disentangled Contextual Adjustment (DCA) and instantiate it as the STEVE framework for better OOD generalization. The proposed model shows superior performance compared to state-of-the-art baselines in comprehensive experiments on real-world datasets.
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by Jiahao Ji,We... alle arxiv.org 03-07-2024
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