Large-scale Network Traffic Prediction With LSTM and Temporal Convolutional Networks
Document Type
Conference Proceeding
Publication Date
1-1-2022
Abstract
Real-time and precise prediction for traffic of networks is critically important for allocating the optimal computing/network resources based on users' business requirements, analyzing the network performance, and realizing intelligent congestion control and high-accuracy anomaly detection. The dramatic growth of users' applications significantly increases the volume, uncertainty, and complexity of workload, thereby making it highly challenging to precisely predict future net-work traffic. Temporal Convolutional Networks (TCNs) and Long Short-Term Memory (LSTM) can be effectively used to analyze and predict time series. This work designs an improved prediction approach for the prediction of network traffic, which combines a Savitzky-Golay filter, TCN, and LSTM, called ST-LSTM for short. It first removes the noise of data with the filter of Savitzky-Golay. It then investigates temporal characteristics of data by using TCN. At last, it investigates the long-term dependency in the time series by using LSTM. Experimental results on a real-life website dataset show the prediction accuracy of ST-LSTM is higher than autoregressive integrated moving average, support vector regression, eXtreme Gradient Boosting, backpropagation, TCN, and LSTM, in terms of several commonly used performance indicators.
Identifier
85136327667 (Scopus)
ISBN
[9781728196817]
Publication Title
Proceedings IEEE International Conference on Robotics and Automation
External Full Text Location
https://doi.org/10.1109/ICRA46639.2022.9812427
ISSN
10504729
First Page
3865
Last Page
3870
Grant
61802015
Fund Ref
National Natural Science Foundation of China
Recommended Citation
Bi, Jing; Yuan, Haitao; Xu, Kangyuan; Ma, Haisen; and Zhou, Meng Chu, "Large-scale Network Traffic Prediction With LSTM and Temporal Convolutional Networks" (2022). Faculty Publications. 3450.
https://digitalcommons.njit.edu/fac_pubs/3450
