SpoT-Mamba: Learning Long-Range Dependency on Spatio-Temporal Graphs with Selective State Spaces
<|reference_start|>SpoT-Mamba: Learning Long-Range Dependency on Spatio-Temporal Graphs with Selective State Spaces: 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.<|reference_end|>
@article{choi2024spot-mamba:,
    title={SpoT-Mamba: Learning Long-Range Dependency on Spatio-Temporal Graphs
  with Selective State Spaces},
    author={Jinhyeok Choi, Heehyeon Kim, Minhyeong An, Joyce Jiyoung Whang},
    journal={arXiv preprint arXiv:2406.11244},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11244},
    primaryClass={cs.LG cs.AI}
}