ZTE Communications ›› 2019, Vol. 17 ›› Issue (2): 19-25.DOI: 10.12142/ZTECOM.201902004
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WANG Shihao, ZHUO Qinzheng, YAN Han, LI Qianmu, QI Yong
Received:
2018-02-13
Online:
2019-06-11
Published:
2019-11-14
About author:
WANG Shihao is a postgraduate student of Nanjing University of Science and Technology, China. He received the bachelor's degree from Nanjing Institute of Technology, China in 2017. His research interests include network traffic prediction and network intrusion detection|ZHUO Qinzheng was a postgraduate student of Nanjing University of Science and Technology, China. He received the bachelor's and master's degree from Nanjing University of Information Science and Technology, China in 2015 and 2018. His research interests include network traffic prediction, data mining, and deep learning|YAN Han received the Ph.D. degree from Nanjing University of Information Science and Technology in 2000. He is an associate professor with Nanjing University of Science and Technology, China. His research interests include software modeling, web computation, information security, and agile software development, and his current focus is on computing system management. He received the second class prizes for national defense science and technology. More than 40 academic papers have been published|LI Qianmu (Supported by:
WANG Shihao, ZHUO Qinzheng, YAN Han, LI Qianmu, QI Yong. A Network Traffic Prediction Method Based on LSTM[J]. ZTE Communications, 2019, 17(2): 19-25.
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URL: https://zte.magtechjournal.com/EN/10.12142/ZTECOM.201902004
Parameter | Value |
---|---|
In size | 1 |
Reservoir size | 1β000 |
Leaking rate | 0.3 |
Spectral radius | 0.136 |
Linear regression algorithm | Ridge regression |
Table 1 Echo status network parameters.
Parameter | Value |
---|---|
In size | 1 |
Reservoir size | 1β000 |
Leaking rate | 0.3 |
Spectral radius | 0.136 |
Linear regression algorithm | Ridge regression |
Parameter | Value |
---|---|
Dropout fraction | 10% |
Time steps | 10 |
RNN units | 200 |
RNN layers | 1 |
Dense units | [ |
Batch size | 10 |
Table 2 LSTM neural network parameters.
Parameter | Value |
---|---|
Dropout fraction | 10% |
Time steps | 10 |
RNN units | 200 |
RNN layers | 1 |
Dense units | [ |
Batch size | 10 |
Date | Time granularity | ESN | LSTM | LSTM and DNN |
---|---|---|---|---|
Date set A | 5 min | 2.81% | 1.41% | 1.39% |
1 h | 7.63% | 4.69% | 4.51% | |
Data set B | 5 min | 3.50% | 3.29% | 1.32% |
1 h | 5.04% | 4.47% | 2.80% | |
Data set C | 5 min | 4.41% | 12.04% | |
1 h | 0.59% | 14.00% |
Table 3 Average absolute percentage error analysis.
Date | Time granularity | ESN | LSTM | LSTM and DNN |
---|---|---|---|---|
Date set A | 5 min | 2.81% | 1.41% | 1.39% |
1 h | 7.63% | 4.69% | 4.51% | |
Data set B | 5 min | 3.50% | 3.29% | 1.32% |
1 h | 5.04% | 4.47% | 2.80% | |
Data set C | 5 min | 4.41% | 12.04% | |
1 h | 0.59% | 14.00% |
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