Temporal Graph MLP Mixer for Spatio-Temporal Forecasting
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.
Code (1)
Tasks
Spatio-Temporal ForecastingTraffic PredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
FlowMixer: A Constrained Neural Architecture for Interpretable Spatiotemporal Forecasting
We introduce FlowMixer, a neural architecture that leverages constrained matrix operations to model structured spatiotemporal patterns. At its core, FlowMixer incorporates non-negative matrix mixing layers within a rever…
Enhancing Topological Dependencies in Spatio-Temporal Graphs with Cycle Message Passing Blocks
Graph Neural Networks (GNNs) and Transformer-based models have been increasingly adopted to learn the complex vector representations of spatio-temporal graphs, capturing intricate spatio-temporal dependencies crucial for…
Spatio-Temporal ForecastingTime Series PredictionTraffic PredictionHieraMix: A Hierarchical MLP-Mixer for Large-Scale Traffic Forecasting
Traffic forecasting task is significant to modern urban management. Recently, there is growing attention on large-scale forecasting, as it better reflects the complexity of real-world traffic networks. However, existing …
Computational EfficiencySTPFormer: A State-of-the-Art Pattern-Aware Spatio-Temporal Transformer for Traffic Forecasting
Spatio-temporal traffic forecasting is challenging due to complex temporal patterns, dynamic spatial structures, and diverse input formats. Although Transformer-based models offer strong global modeling, they often strug…
Representation LearningGraph MatchingPreMixer: MLP-Based Pre-training Enhanced MLP-Mixers for Large-scale Traffic Forecasting
In urban computing, precise and swift forecasting of multivariate time series data from traffic networks is crucial. This data incorporates additional spatial contexts such as sensor placements and road network layouts, …
Computational EfficiencyTime Series