Network traffic prediction based on improved support vector machine
Qi-ming Wang (),
Ai-wan Fan and
He-sheng Shi
Additional contact information
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
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://link.springer.com/10.1007/s13198-016-0412-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:8:y:2017:i:3:d:10.1007_s13198-016-0412-8
Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198
DOI: 10.1007/s13198-016-0412-8
Access Statistics for this article
International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar
More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().