🤖 AI Summary
Addressing spatiotemporal forecasting under high sensor missingness (e.g., in traffic and meteorology), this paper proposes a novel graph-enhanced spatiotemporal modeling architecture. Methodologically, it introduces a first-of-its-kind 3D MLP-Mixer structure, integrated with graph-structure-aware patch-level subgraph partitioning encoding, jointly capturing local spatial dependencies and long-range spatiotemporal–feature correlations. A missingness-aware mechanism is further incorporated to enhance robustness under sparse observations. Experiments on four benchmark datasets—AQI, ENGRAD, PV-US, and METR-LA—demonstrate that the method significantly outperforms state-of-the-art approaches under high missingness (>50%), especially in long-horizon forecasting (12–24 steps), exhibiting superior generalization and enhanced long-range dependency modeling. The core contribution lies in the deep integration of subgraph partitioning encoding with spatiotemporal MLP-Mixer, establishing a scalable and highly robust paradigm for missingness-intensive spatiotemporal forecasting.
📝 Abstract
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.