Abstract
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 language | English (US) |
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Journal | IEEE Transactions on Automation Science and Engineering |
DOIs | |
State | Accepted/In press - 2021 |
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering
Keywords
- 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).