Regional Correlation Aided Mobile Traffic Prediction with Spatiotemporal
  Deep Learning
JeongJun Park Lusungu J. Mwasinga Huigyu Yang Syed M. Raza Duc-Tai Le Moonseong Kim Min Young Chung Hyunseung Choo
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.