Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Network for Traffic Forecasting

• URL: http://arxiv.org/abs/2111.00724v1
• Date: Mon, 1 Nov 2021 06:47:42 GMT
• Title: Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Network for Traffic Forecasting
• Authors: Xing Wang (1), Juan Zhao (1), Lin Zhu (1), Xu Zhou (2), Zhao Li (2), Junlan Feng (1), Chao Deng (1), Yong Zhang (2) ((1) China Mobile Research Institute, Beijing, China, (2) Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China)
• Abstract summary: We propose a novel deep learning network architecture to model the traffic dynamics of mobile base stations. Experiments on four real-world datasets consistently show AMF-STGCN outperforms the state-of-the-art methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mobile network traffic forecasting is one of the key functions in daily network operation. A commercial mobile network is large, heterogeneous, complex and dynamic. These intrinsic features make mobile network traffic forecasting far from being solved even with recent advanced algorithms such as graph convolutional network-based prediction approaches and various attention mechanisms, which have been proved successful in vehicle traffic forecasting. In this paper, we cast the problem as a spatial-temporal sequence prediction task. We propose a novel deep learning network architecture, Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Networks (AMF-STGCN), to model the traffic dynamics of mobile base stations. AMF-STGCN extends GCN by (1) jointly modeling the complex spatial-temporal dependencies in mobile networks, (2) applying attention mechanisms to capture various Receptive Fields of heterogeneous base stations, and (3) introducing an extra decoder based on a fully connected deep network to conquer the error propagation challenge with multi-step forecasting. Experiments on four real-world datasets from two different domains consistently show AMF-STGCN outperforms the state-of-the-art methods.

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