Spatial-temporal forecasting has various applications in transportation, climate, and human activity domains. Current spatial-temporal forecasting models primarily adopt a macro perspective, focusing on achieving strong overall prediction performance for the entire system. However, most of these models overlook the importance of enhancing the uniformity of prediction performance across different nodes, leading to poor prediction capabilities for certain nodes and rendering some results impractical. This task is particularly challenging due to the inherent heterogeneity of spatial-temporal data. To address this issue, in this paper, we propose a novel Heterogeneity-informed Mixture-of-Experts (HiMoE) for fair spatial-temporal forecasting. Specifically, we design a Heterogeneity-Informed Graph Convolutional Network (HiGCN), integrated into each expert model to enhance the flexibility of the experts. To adapt to the heterogeneity of spatial-temporal data, we design a Node-wise Mixture-of-Experts (NMoE). This model decouples the spatial-temporal prediction task into sub-tasks at the spatial scale, which are then assigned to different experts. To allocate these sub-tasks, we use a mean-based graph decoupling method to distinguish the graph structure for each expert. The results are then aggregated using an output gating mechanism based on a dense Mixture-of-Experts (dMoE). Additionally, fairness-aware loss and evaluation functions are proposed to train the model with uniformity and accuracy as objectives. Experiments conducted on four datasets, encompassing diverse data types and spatial scopes, validate HiMoE's ability to scale across various real-world scenarios. Furthermore, HiMoE consistently outperforms baseline models, achieving superior performance in both accuracy and uniformity.

翻译：时空预测在交通、气候和人类活动等领域具有广泛应用。当前的时空预测模型主要采用宏观视角，侧重于为整个系统实现强大的整体预测性能。然而，这些模型大多忽视了提升不同节点间预测性能一致性的重要性，导致对某些节点的预测能力较差，使得部分结果不切实际。由于时空数据固有的异质性，这项任务尤其具有挑战性。为解决此问题，本文提出了一种新颖的异质性感知专家混合模型（HiMoE），用于实现公平的时空预测。具体而言，我们设计了一种异质性感知图卷积网络（HiGCN），将其集成到每个专家模型中，以增强专家的灵活性。为适应时空数据的异质性，我们设计了一种节点级专家混合模型（NMoE）。该模型将时空预测任务在空间尺度上解耦为子任务，随后分配给不同的专家。为分配这些子任务，我们采用基于均值的图解耦方法来区分每个专家的图结构。结果随后通过基于密集专家混合模型（dMoE）的输出门控机制进行聚合。此外，我们提出了公平性感知的损失函数和评估函数，以一致性和准确性为目标训练模型。在涵盖不同数据类型和空间范围的四个数据集上进行的实验验证了HiMoE能够适应各种现实场景。进一步地，HiMoE在准确性和一致性方面均持续优于基线模型，实现了更优的性能。