  Telecommunication networks play a critical role in modern society. With the
arrival of 5G networks, these systems are becoming even more diversified,
integrated, and intelligent. Traffic forecasting is one of the key components
in such a system, however, it is particularly challenging due to the complex
spatial-temporal dependency. In this work, we consider this problem from the
aspect of a cellular network and the interactions among its base stations. We
thoroughly investigate the characteristics of cellular network traffic and shed
light on the dependency complexities based on data collected from a densely
populated metropolis area. Specifically, we observe that the traffic shows both
dynamic and static spatial dependencies as well as diverse cyclic temporal
patterns. To address these complexities, we propose an effective
deep-learning-based approach, namely, Spatio-Temporal Hybrid Graph
Convolutional Network (STHGCN). It employs GRUs to model the temporal
dependency, while capturing the complex spatial dependency through a hybrid-GCN
from three perspectives: spatial proximity, functional similarity, and recent
trend similarity. We conduct extensive experiments on real-world traffic
datasets collected from telecommunication networks. Our experimental results
demonstrate the superiority of the proposed model in that it consistently
outperforms both classical methods and state-of-the-art deep learning models,
while being more robust and stable.
