Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction
Network traffic prediction plays a vital role in effective network management, load evaluation and security warning. Extreme learning machine has the advantages of fast convergence speed and strong generalization ability. Also, it does not easily fall into local optima. The evolutionary algorithm ca...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9388647/ |
| _version_ | 1811273956943986688 |
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| author | Jinmei Shi Yu-Beng Leau Kun Li Huandong Chen |
| author_facet | Jinmei Shi Yu-Beng Leau Kun Li Huandong Chen |
| author_sort | Jinmei Shi |
| collection | DOAJ |
| description | Network traffic prediction plays a vital role in effective network management, load evaluation and security warning. Extreme learning machine has the advantages of fast convergence speed and strong generalization ability. Also, it does not easily fall into local optima. The evolutionary algorithm can be used to optimize the number of its hidden layer nodes. However, most of the existing evolutionary algorithms have some adjustable parameters which depend on subjective experience or prior knowledge. Hence, this can affect the model prediction accuracy. Given this context, this paper proposes a network traffic prediction mechanism based on optimized Variational Mode Decomposition (VMD) and Integrated Extreme Learning Machine (ELM). A Scalable Artificial Bee Colony (SABC) algorithm which has fewer adjustable parameters and can thus guarantee the accuracy and stability of the prediction mechanism is also proposed. It can be used in the optimization selection of VMD, Phase Space Reconstruction (PSR) and ELM to achieve higher prediction performance. Finally, we utilize Mackey-Glass, Lorenz chaotic time series of recognized benchmark and a WIDE backbone actual network traffic data to prove the validity of the proposed network traffic prediction mechanism. |
| first_indexed | 2024-04-12T23:09:04Z |
| format | Article |
| id | doaj.art-1fcdb1f049af486985c02bdfb6d67eec |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T23:09:04Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1fcdb1f049af486985c02bdfb6d67eec2022-12-22T03:12:50ZengIEEEIEEE Access2169-35362021-01-019518185183110.1109/ACCESS.2021.30692809388647Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic PredictionJinmei Shi0https://orcid.org/0000-0002-9021-5464Yu-Beng Leau1https://orcid.org/0000-0002-5386-2734Kun Li2https://orcid.org/0000-0002-4632-9050Huandong Chen3Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, MalaysiaFaculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, MalaysiaFaculty of Electrical and Control Engineering, Liaoning Technical University, Liaoning, ChinaCollege of Information Science and Technology, Hainan Normal University, Haikou, ChinaNetwork traffic prediction plays a vital role in effective network management, load evaluation and security warning. Extreme learning machine has the advantages of fast convergence speed and strong generalization ability. Also, it does not easily fall into local optima. The evolutionary algorithm can be used to optimize the number of its hidden layer nodes. However, most of the existing evolutionary algorithms have some adjustable parameters which depend on subjective experience or prior knowledge. Hence, this can affect the model prediction accuracy. Given this context, this paper proposes a network traffic prediction mechanism based on optimized Variational Mode Decomposition (VMD) and Integrated Extreme Learning Machine (ELM). A Scalable Artificial Bee Colony (SABC) algorithm which has fewer adjustable parameters and can thus guarantee the accuracy and stability of the prediction mechanism is also proposed. It can be used in the optimization selection of VMD, Phase Space Reconstruction (PSR) and ELM to achieve higher prediction performance. Finally, we utilize Mackey-Glass, Lorenz chaotic time series of recognized benchmark and a WIDE backbone actual network traffic data to prove the validity of the proposed network traffic prediction mechanism.https://ieeexplore.ieee.org/document/9388647/Network traffictime series analysisvariational mode decompositionextreme learning machineartificial bee colony algorithm |
| spellingShingle | Jinmei Shi Yu-Beng Leau Kun Li Huandong Chen Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction IEEE Access Network traffic time series analysis variational mode decomposition extreme learning machine artificial bee colony algorithm |
| title | Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction |
| title_full | Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction |
| title_fullStr | Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction |
| title_full_unstemmed | Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction |
| title_short | Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction |
| title_sort | optimal variational mode decomposition and integrated extreme learning machine for network traffic prediction |
| topic | Network traffic time series analysis variational mode decomposition extreme learning machine artificial bee colony algorithm |
| url | https://ieeexplore.ieee.org/document/9388647/ |
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