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Network traffic prediction based on improved support vector machine

Author

Listed:
  • Qi-ming Wang

    (Pingdingshan University)

  • Ai-wan Fan

    (Pingdingshan University)

  • He-sheng Shi

    (Pingdingshan University)

Abstract

Network traffic is featured by non-linear time-varying and chaos, and the existing prediction models based on support vector machine (SVM) have low stability and precision. We adopt fuzzy analytic hierarchy process to improve the SVM-based prediction model by first optimizing the parameters $$\sigma$$ σ and $$C$$ C . Then SVM is trained using the optimal parameters, and the prediction model is built to forecast the network traffic. Experiment shows that the proposed algorithm cannot only track the variation trend of network traffic, but also achieve an accurate prediction with very small fluctuation of prediction error. Thus SVM-based model has high precision in predicting network traffic.

Suggested Citation

  • Qi-ming Wang & Ai-wan Fan & He-sheng Shi, 2017. "Network traffic prediction based on improved support vector machine," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(3), pages 1976-1980, November.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:3:d:10.1007_s13198-016-0412-8
    DOI: 10.1007/s13198-016-0412-8
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    Cited by:

    1. Leina Zhao & Yujia Bai & Sishi Zhang & Yanpeng Wang & Jie Kang & Wenxuan Zhang, 2022. "A Novel Hybrid Model for Short-Term Traffic Flow Prediction Based on Extreme Learning Machine and Improved Kernel Density Estimation," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
    2. Ya Li & Zhanguo Wei, 2022. "Regional Logistics Demand Prediction: A Long Short-Term Memory Network Method," Sustainability, MDPI, vol. 14(20), pages 1-17, October.

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