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VaryST: Generalizable Spatio-Temporal Forecasting Beyond the Closed-World Setting
Spatio-temporal forecasting is fundamental to many real-world systems, yet deployment environments are inherently varying: temporal distributions may evolve, spatial domains may change, and observation availability may shift from training conditions. Existing models are often tightly coupled to thei
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