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

• Published in: IEEE transactions on automation science and engineering Vol. 19; no. 3; pp. 1 - 11
• Main Authors: Bi, Jing, Zhang, Xiang, Yuan, Haitao, Zhang, Jia, Zhou, MengChu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Cloud computing; Communications traffic; Data centers; Datasets; Deep learning; Edge computing; Feature extraction; Industrial areas; Load modeling; Long short-term memory (LSTM); machine learning; network traffic prediction; Neural networks; Noise prediction; Predictive models; Real time; Savitzky-Golay (SG) filter; Sequences; Support vector machines; Task analysis; temporal convolutional network (TCN); Time series; Time series analysis
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2021.3077537

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.