新模型T-GMM用图结构和三维MLP混合器，提升传感器缺失数据下的时空预测能力。
Temporal Graph MLP Mixer for Spatio-Temporal Forecasting
- 分节点和子图块处理，结合三维MLP混合器捕捉时空特征
- 在四个真实数据集上表现稳健，尤其擅长长程依赖建模
- 适合处理传感器常缺数的交通、气象等场景
时空预测在交通预测、气候建模和环境监测等应用中至关重要。然而，真实传感器网络中普遍存在的数据缺失显著增加了该任务的难度。本文提出一种名为时间图MLP混合器（Temporal Graph MLP-Mixer, T-GMM）的新架构，结合节点级处理与块级子图编码，以捕捉局部空间依赖性，并利用三维MLP-Mixer同时建模时间、空间与特征维度的依赖关系。在AQI、ENGRAD、PV-US和METR-LA四个数据集上的实验表明，该模型即使在存在大量缺失数据的情况下仍具备良好的预测能力。尽管在所有场景下未全面超越当前最优模型，但T-GMM展现出较强的建模能力，尤其在捕捉长程依赖方面表现突出。结果表明其在鲁棒性和可扩展性方面具有潜力，适用于实际复杂环境中的时空预测任务。
原文摘要 · Abstract (English)
Spatiotemporal forecasting is critical in applications such as traffic prediction, climate modeling, and environmental monitoring. However, the prevalence of missing data in real-world sensor networks significantly complicates this task. In this paper, we introduce the Temporal Graph MLP-Mixer (T-GMM), a novel architecture designed to address these challenges. The model combines node-level processing with patch-level subgraph encoding to capture localized spatial dependencies while leveraging a three-dimensional MLP-Mixer to handle temporal, spatial, and feature-based dependencies. Experiments on the AQI, ENGRAD, PV-US and METR-LA datasets demonstrate the model's ability to effectively forecast even in the presence of significant missing data. While not surpassing state-of-the-art models in all scenarios, the T-GMM exhibits strong learning capabilities, particularly in capturing long-range dependencies. These results highlight its potential for robust, scalable spatiotemporal forecasting.
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