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Self-Supervised Deconfounding Against Spatio-Temporal Shifts: Theory and Modeling
ST traffic forecasting models need to address out-of-distribution issues caused by spatio-temporal shifts and external factors, such as time and weather variations.
Self-Supervised Deconfounding Against Spatio-Temporal Shifts: Theory and Modeling
The authors propose a theoretical solution named Disentangled Contextual Adjustment (DCA) to address the out-of-distribution (OOD) issue in traffic data forecasting. They introduce the Spatio-Temporal sElf-superVised dEconfounding (STEVE) framework to improve generalization ability.
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