A Hybrid Prediction Method for Realistic Network Traffic With Temporal Convolutional Network and LSTM

作者：Jing Bi, Xiang Zhang, Haitao Yuan, Jia Zhang, MengChu Zhou · 发表于：IEEE Transactions on Automation Science and Engineering · 年份：2021 · DOI：10.1109/tase.2021.3077537 · 被引用次数：330 · 研究领域：Traffic Prediction and Management Techniques、Traffic control and management、Energy Load and Power Forecasting

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.Note to Practitioners—This work considers real-time and high-accuracy prediction of network traffic. It is highly important to well predict network traffic by capturing long-term dependence and effectively extracting high- and low-frequency information from time series data. Yet, it is a big challenge to achieve it because there are unstable characteristics and stron...