基于MGU的大规模IP骨干网络实时流量预测

为克服长短时记忆网络(long short-term memory, LSTM)计算成本相当大的弊端，提出基于最小门控单元(minimal gated unit, MGU)的大规模IP骨干网络实时流量预测方法。试验结果表明，与基于LSTM的流量预测方法相比，该方法以较少的模型训练时间获得了相当甚至略优的流量预测性能，在流量预测精度和实时性方面也优于已有的前馈神经网络(feed forward neural network, FFNN)和门控循环单元神经网络(gated recurrent unit, GRU)方法。

Real-time traffic prediction based on MGU for large-scale IP backbone networks

In order to overcome the shortcomings of long short-term memory (LSTM) computing cost, a real-time traffic prediction method based on minimum gated unit (MGU) for large-scale IP backbone networks was proposed. The experimental results showed that compared with the LSTM-based traffic prediction method, the proposed method achieved fairly or even better traffic prediction performance with less model training time, meanwhile it outperformed the most advanced feed forward neural network (FFNN), LSTM and gated recurrent unit(GRU) in terms of prediction accuracy and real-time performance.