论文标题
使用经常性神经网络预测Internet活动的短期移动互联网流量
Predicting Short-term Mobile Internet Traffic from Internet Activity using Recurrent Neural Networks
论文作者
论文摘要
移动网络流量预测是网络容量计划和优化的重要输入。现有方法可能缺乏计算爆发，非线性模式或时间序列移动网络数据中的其他重要相关性的速度和计算复杂性。我们比较了两个深度学习体系结构的性能 - 长期短期记忆（LSTM）和封闭式复发单元（GRU） - 使用两个月的米兰都会区的意大利电信数据来预测移动互联网流量。基于Internet活动，使用了K-均值聚类对组细胞的先验性，并使用网格搜索方法来识别每个模型的最佳配置。使用均方根误差评估模型的预测质量。两种深度学习算法在几天内和两个月内都可以有效地对互联网活动和季节性进行建模。我们发现城市内部集群之间的性能变化。总体而言，LSTM在我们的实验中表现优于GRU。
Mobile network traffic prediction is an important input in to network capacity planning and optimization. Existing approaches may lack the speed and computational complexity to account for bursting, non-linear patterns or other important correlations in time series mobile network data. We compare the performance of two deep learning architectures - Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) - for predicting mobile Internet traffic using two months of Telecom Italia data for the metropolitan area of Milan. K-Means clustering was used a priori to group cells based on Internet activity and the Grid Search method was used to identify the best configurations for each model. The predictive quality of the models was evaluated using root mean squared error. Both Deep Learning algorithms were effective in modeling Internet activity and seasonality, both within days and across two months. We find variations in performance across clusters within the city. Overall, the LSTM outperformed the GRU in our experiments.
