Network traffic prediction based on improved support vector machine

Qi-ming Wang (), Ai-wan Fan and He-sheng Shi
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Qi-ming Wang: Pingdingshan University
Ai-wan Fan: Pingdingshan University
He-sheng Shi: Pingdingshan University

International Journal of System Assurance Engineering and Management, 2017, vol. 8, issue 3, No 4, 1976-1980

Abstract: 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.

Keywords: Support vector machine (SVM); Network traffic prediction; Fuzzy analytic hierarchy process (FAHP); Parameter optimization; Prediction model (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s13198-016-0412-8

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