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Abstract
The network traffic prediction is important for service quality control in computer network. The performance of the traditional prediction method significantly degrades for the burst short-term flow. In view of the problem, this paper proposes a double LSTMs structure, one of which acts as the main flow predictor, another as the detector of the time the burst flow starts at. The two LSTM units can exchange information about their internal states, and the predictor uses the detector’s information to improve the accuracy of the prediction. A training algorithm is developed specially to train the structure offline. To obtain the prediction online, a pulse series is used as a simulant of the burst event. A simulation experiment is designed to test performance of the predictor. The results of the experiment show that the prediction accuracy of the double LSTM structure is significantly improved, compared with the traditional single LSTM structure.
This work was supported by the research plan of State Grid Sichuan Electric Power Company, China,and supported by the research plan of the 10th Research Institute of China Electronics Technology Group Corporation (KTYT-XY-002).
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Authors and Affiliations
State Grid Sichuan Information and Communication Company, Chengdu, 610041, China
Lin Huang, Diangang Wang & Xiao Liu
University of Electronic Science and Technology of China, Chengdu, 611731, China
Yongning Zhuo
The 10th Research Institute of China Electronics Technology Group Corporation, Chengdu, 610036, China
Yong Zeng
- Lin Huang
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- Diangang Wang
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- Xiao Liu
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- Yongning Zhuo
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- Yong Zeng
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Correspondence toYongning Zhuo.
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North University of China, Taiyuan, China
Jianchao Zeng
Northeast Forestry University, Harbin, China
Weipeng Jing
Harbin University of Science and Technology, Harbin, China
Xianhua Song
National Academy of Guo Ding Institute of Data Science, Beijing, China
Zeguang Lu
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Huang, L., Wang, D., Liu, X., Zhuo, Y., Zeng, Y. (2020). Double LSTM Structure for Network Traffic Flow Prediction. In: Zeng, J., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1257. Springer, Singapore. https://doi.org/10.1007/978-981-15-7981-3_27
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