Cellular traffic prediction plays an important role in network management and resource utilization. However, due to the high nonlinearity and dynamic spatial-temporal correlation, it is challenging to obtain the traffic prediction accurately. In this letter, a spatial-temporal attention-convolution network is proposed to predict the citywide cellular traffic. Considering the temporal correlation of the cellular traffic, the traffic data is modeled by the hourly, daily and weekly traffic components independently. In each traffic component, the spatial-temporal attention module is designed to capture the dynamic spatial-temporal correlation of cellular traffic; the spatial-temporal convolution module utilizes the graph convolution and standard convolution to obtain the spatial and temporal features simultaneously. By considering the external factors, the prediction of final mobile traffic is obtained. Experiment results indicate that the proposed approach outperforms the existing prediction methods on the real-world cellular traffic datasets.

中文翻译：

---

用于全市蜂窝交通预测的时空注意力卷积网络

蜂窝流量预测在网络管理和资源利用方面起着重要作用。然而，由于高度非线性和动态时空相关性，准确获得交通预测具有挑战性。在这封信中，提出了一个时空注意力卷积网络来预测全市蜂窝交通。考虑到蜂窝流量的时间相关性，流量数据由每小时、每天和每周的流量分量独立建模。在每个交通组件中，时空注意力模块旨在捕捉细胞交通的动态时空相关性；时空卷积模块利用图卷积和标准卷积同时获取时空特征。综合考虑外部因素，得到最终移动流量的预测。实验结果表明，所提出的方法在现实世界的蜂窝交通数据集上优于现有的预测方法。