A Hybrid Prediction Method for Realistic Network Traffic With Temporal Convolutional Network and LSTM
A Hybrid Prediction Method for Realistic Network Traffic With Temporal Convolutional Network and LSTM
复制标题
时域卷积网络和 LSTM 的真实网络流量混合预测方法
DOI：
10.1109/tase.2021.3077537
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发表时间：
2021-05-20
影响因子：
5.6
通讯作者：
Zhou, MengChu
中科院分区：
文献类型：
--
作者：
Bi, Jing;Zhang, Xiang;Zhou, MengChu
准确、实时的网络流量预测不仅可以帮助系统运营商根据实际业务需求合理分配资源，还可以帮助他们评估网络的性能，分析网络的健康状况。近年来，神经网络已被证明适用于预测时间序列数据，由长短期记忆（LSTM）神经网络和时间卷积网络（TCN）模型表示。本文提出了一种新的混合预测方法，称为SG和基于TCN的LSTM（ST-LSTM），用于这种网络流量预测，它协同结合了Savitzky-Golay（SG）滤波器，TCN以及LSTM的功能。ST-LSTM采用三阶段的端到端方法来进行时间序列预测。它首先使用SG滤波器消除原始数据中的噪声，然后应用TCN从序列中提取短期特征，然后利用LSTM捕获数据中的长期依赖性。在真实世界数据集上的实验结果表明，所提出的ST-LSTM在预测精度方面优于最先进的算法。
Accurate and real-time prediction of network traffic can not only help system operators allocate resources rationally according to their actual business needs but also help them assess the performance of a network and analyze its health status. In recent years, neural networks have been proved suitable to predict time series data, represented by the model of a long short-term memory (LSTM) neural network and a temporal convolutional network (TCN). This article proposes a novel hybrid prediction method named SG and TCN-based LSTM (ST-LSTM) for such network traffic prediction, which synergistically combines the power of the Savitzky-Golay (SG) filter, the TCN, as well as the LSTM. ST-LSTM employs a three-phase end-to-end methodology serving time series prediction. It first eliminates noise in raw data using the SG filter, then extracts short-term features from sequences applying the TCN, and then captures the long-term dependence in the data exploiting the LSTM. Experimental results over real-world datasets demonstrate that the proposed ST-LSTM outperforms state-of-the-art algorithms in terms of prediction accuracy.