  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.
