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

Jing Bi, Xiang Zhang, Haitao Yuan, Jia Zhang, Meng Chu Zhou

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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.

Original languageEnglish (US)
JournalIEEE Transactions on Automation Science and Engineering
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


  • Deep learning
  • Feature extraction
  • Load modeling
  • Long short-term memory (LSTM)
  • Predictive models
  • Savitzky-Golay (SG) filter
  • Support vector machines
  • Task analysis
  • Time series analysis
  • machine learning
  • network traffic prediction
  • temporal convolutional network (TCN).


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