提出高效多模态时空预测框架，解决信息融合与因果干扰问题。
Causal Spatio-Temporal Prediction: An Effective and Efficient Multi-Modal Approach
- 用跨模态注意力和门控机制融合多源数据
- 双分支因果推理提升预测准确率，最高提9.66%
- 结合GCN与Mamba加速计算，降耗超17%
时空预测在智能交通、天气预报和城市规划中至关重要。尽管融合多模态数据有望提升预测精度，但仍面临三大挑战：(i) 多模态信息融合不足，(ii) 混淆因素掩盖真实因果关系，(iii) 预测模型计算复杂度高。为此，我们提出E^2-CSTP——一种高效且有效的因果多模态时空预测框架。E^2-CSTP利用跨模态注意力和门控机制有效整合多模态数据；在此基础上，设计双分支因果推断方法：主分支聚焦时空预测，辅分支通过建模额外模态并施加因果干预，揭示真实因果依赖以减少偏差。为提升效率，将GCN与Mamba架构结合，实现加速的时空编码。在4个真实数据集上的大量实验表明，E^2-CSTP显著优于9种先进方法，在准确率上最高提升9.66%，计算开销降低17.37%–56.11%。
原文摘要 · Abstract (English)
Spatio-temporal prediction plays a crucial role in intelligent transportation, weather forecasting, and urban planning. While integrating multi-modal data has shown potential for enhancing prediction accuracy, key challenges persist: (i) inadequate fusion of multi-modal information, (ii) confounding factors that obscure causal relations, and (iii) high computational complexity of prediction models. To address these challenges, we propose E^2-CSTP, an Effective and Efficient Causal multi-modal Spatio-Temporal Prediction framework. E^2-CSTP leverages cross-modal attention and gating mechanisms to effectively integrate multi-modal data. Building on this, we design a dual-branch causal inference approach: the primary branch focuses on spatio-temporal prediction, while the auxiliary branch mitigates bias by modeling additional modalities and applying causal interventions to uncover true causal dependencies. To improve model efficiency, we integrate GCN with the Mamba architecture for accelerated spatio-temporal encoding. Extensive experiments on 4 real-world datasets show that E^2-CSTP significantly outperforms 9 state-of-the-art methods, achieving up to 9.66% improvements in accuracy as well as 17.37%-56.11% reductions in computational overhead.
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