  Spatio-temporal graph (STG) forecasting is a critical task with extensive
applications in the real world, including traffic and weather forecasting.
Although several recent methods have been proposed to model complex dynamics in
STGs, addressing long-range spatio-temporal dependencies remains a significant
challenge, leading to limited performance gains. Inspired by a recently
proposed state space model named Mamba, which has shown remarkable capability
of capturing long-range dependency, we propose a new STG forecasting framework
named SpoT-Mamba. SpoT-Mamba generates node embeddings by scanning various
node-specific walk sequences. Based on the node embeddings, it conducts
temporal scans to capture long-range spatio-temporal dependencies. Experimental
results on the real-world traffic forecasting dataset demonstrate the
effectiveness of SpoT-Mamba.
