Jiating Li
ABSTRACT
In order to improve the quality of network services, help rationalize the allocation of network bandwidth resources, and ensure the security of network operation, a better network traffic forecast is required. To address the problem that a single predictive model does not accurately forecast network traffic with complex characteristics, we propose a combination forecasting model combining decomposition techniques with a single forecasting model. By predicting real network traffic, the findings show that the CEEMD-ELM works can improve the performance of network traffic forecasting. It is highly reliable compared with the single ELM model and has a stronger prediction capability, which can make more accurate predictions of network traffic.
- Yang, Hanyu, Xutao Li, Wenhao Qiang, Yuhan Zhao, Wei Zhang, and Chang Tang. “A Network Traffic Forecasting Method Based on SA Optimized Arima–BP Neural Network.” Computer Networks 193, 2021: 108102.Google Scholar
Cross Ref
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- Wu, Jiang, Yu Chen, Tengfei Zhou, and Taiyong Li. “An Adaptive Hybrid Learning Paradigm Integrating CEEMD, Arima and SBL for Crude Oil Price Forecasting.” Energies 12, no. 7, 2019: 1239.Google ScholarCross Ref
- Niu, Dongxiao, Keke Wang, Lijie Sun, Jing Wu, and Xiaomin Xu. “Short-Term Photovoltaic Power Generation Forecasting Based on Random Forest Feature Selection and CEEMD: A Case Study.” Applied Soft Computing 93, 2020: 106389.Google ScholarCross Ref
- Zhang, Junrong, Huiming Tang, Dwayne D. Tannant, Chengyuan Lin, Ding Xia, Xiao Liu, Yongquan Zhang, and Junwei Ma. “Combined Forecasting Model with CEEMD-LCSS Reconstruction and the ABC-SVR Method for Landslide Displacement Prediction.” Journal of Cleaner Production 293, 2021: 126205.Google ScholarCross Ref
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Index Terms
- Combinatorial model based on extreme learning machine for network traffic prediction
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