Mobile traffic data in urban regions shows differentiated patterns during
different hours of the day. The exploitation of these patterns enables highly
accurate mobile traffic prediction for proactive network management. However,
recent Deep Learning (DL) driven studies have only exploited spatiotemporal
features and have ignored the geographical correlations, causing high
complexity and erroneous mobile traffic predictions. This paper addresses these
limitations by proposing an enhanced mobile traffic prediction scheme that
combines the clustering strategy of daily mobile traffic peak time and novel
multi Temporal Convolutional Network with a Long Short Term Memory (multi
TCN-LSTM) model. The mobile network cells that exhibit peak traffic during the
same hour of the day are clustered together. Our experiments on large-scale
real-world mobile traffic data show up to 28% performance improvement compared
to state-of-the-art studies, which confirms the efficacy and viability of the
proposed approach.

提出了一种增强型移动流量预测方案，该方案结合了每日移动流量高峰时间的聚类策略和新颖的多时间卷积网络与长短期记忆模型 (multi TCN-LSTM)，实验结果显示与最先进技术相比，性能提升达到 28％，证实了该方法的功效和可行性。