Earticle

초록

영어

Network traffic is a typical time-series data with strong lag and aftereffect, for the existence of local optimum, time-consuming and other defects in the method for the currently determining number of lags, this paper presents a combination of network traffic prediction method (GS-GA-LSSVM). At first, using geo-statistics (GS) to quickly determine the optimal lag order of network traffic, then reconstructing network traffic with the lag order, and finally using genetic algorithm (GA) to optimize least squares supporting vector machine (LSSVM) to model predictions for network traffic. Simulation results show that, GS-GA-LSSVM on network traffic prediction accuracy is better than any of the participating models, and which can better reflect the complex dynamics of network traffic disciplines.

목차

Abstract
 1. Introduction
 2. Review of Geo-statistics and Least Square Supporting Vector Machine
  2.1. Geo-statistics(GS)
  2.2. Least Squares Supporting Vector Machines
  2.3. Parameter Optimization in Least Squares Supporting Vector Machines
 3. Network Flow Forecast Model of GS-LSSVM
  3.1. Idea of GS Combining with LSSVM
  3.2. Data Normalization
  3.3. LSSVM Model Order Determined based on GS
  3.4. LSSVM Pre-modeling Network Flow after Reconstruction
 4. Evaluation Results and Discussion of GS-LSSVM in Network Traffic Prediction
  4.1. Data Sources
  4.2. Evaluation Index and Compared Models
  4.3. Model Implementation
  4.4. Performance Compared with other Models
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Xianmin Wei [ School of Computer Engineering, Weifang University 5147 Eastern Dongfeng Street, Weifang 261061,China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605