School of Information Engineering, China University of Geosciences, Beijing, China.
PLoS One. 2023 Sep 8;18(9):e0288935. doi: 10.1371/journal.pone.0288935. eCollection 2023.
Accurately predicting mobile network traffic can help mobile network operators allocate resources more rationally and can facilitate stable and fast network services to users. However, due to burstiness and uncertainty, it is difficult to accurately predict network traffic.
Considering the spatio-temporal correlation of network traffic, we proposed a deep-learning model, Convolutional Block Attention Module (CBAM) Spatio-Temporal Convolution Network-Transformer, for time-series prediction based on a CBAM attention mechanism, a Temporal Convolutional Network (TCN), and Transformer with a sparse self-attention mechanism. The model can be used to extract the spatio-temporal features of network traffic for prediction. First, we used the improved TCN for spatial information and added the CBAM attention mechanism, which we named CSTCN. This model dealt with important temporal and spatial features in network traffic. Second, Transformer was used to extract spatio-temporal features based on the sparse self-attention mechanism. The experiments in comparison with the baseline showed that the above work helped significantly to improve the prediction accuracy. We conducted experiments on a real network traffic dataset in the city of Milan.
The results showed that CSTCN-Transformer reduced the mean square error and the mean average error of prediction results by 65.16%, 64.97%, and 60.26%, and by 51.36%, 53.10%, and 38.24%, respectively, compared to CSTCN, a Long Short-Term Memory network, and Transformer on test sets, which justified the model design in this paper.
准确预测移动网络流量有助于移动网络运营商更合理地分配资源，并为用户提供稳定、快速的网络服务。然而，由于突发性和不确定性，网络流量很难准确预测。
考虑到网络流量的时空相关性，我们提出了一种基于卷积注意力模块（CBAM）注意力机制、时间卷积网络（TCN）和稀疏自注意力机制的Transformer 的深度学习模型，用于时间序列预测。该模型可用于提取网络流量的时空特征进行预测。首先，我们使用改进的 TCN 进行空间信息处理，并添加了 CBAM 注意力机制，我们将其命名为 CSTCN。该模型处理了网络流量中的重要时空特征。其次，Transformer 用于基于稀疏自注意力机制提取时空特征。与基线的实验对比表明，上述工作有助于显著提高预测精度。我们在米兰市的真实网络流量数据集上进行了实验。
结果表明，与 CSTCN、长短期记忆网络（LSTM）和 Transformer 相比，CSTCN-Transformer 在测试集上的预测结果的均方误差和平均绝对误差分别降低了 65.16%、64.97%和 60.26%，降低了 51.36%、53.10%和 38.24%，证明了本文模型设计的合理性。
