Advertisement

Holidays Busy Traffic Forecasting Based on MPSO-SVR Algorithm

  • Jiao Lan
  • DianJun Li
  • XiZhong Qin
  • ZhenHong Jia
  • Li Chen
  • Lei Sheng
  • Hong Li
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 169)

Abstract

Holiday traffic prediction is the foundation of the whole communication network planning. In order to predict the busy traffic accurately and ensure the stability of the network, a support vector regression machine (SVR) combined with the improved particle swarm optimization algorithm (MPSO) is proposed, an inertia weight and shrinkage factor is introduced in the algorithm. The proposed algorithm is used to predict the busy traffic in Mid-autumn day. Simulation result shows that, compared with SVR algorithm and the basic particle swarm optimization optimize SVR (PSO-SVR) method, MPSO-SVR algorithm has a higher prediction precision.

Keywords

busy traffic forecasting support vector regression machine improved particle swarm optimization algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Elattar, E.E., Goulermas, J.(Y.), Wu, Q.H.: Electric Load Forecasting Based on Locally Weighted Support Vector Regression. IEEE Transaction on Systems 40, 438–447 (2010)Google Scholar
  2. 2.
    Wang, J., Zhao, H.: ε-SVRM parameters optimization based on the difference of evolution. Computer Application 28, 2074–2076 (2008)MATHCrossRefGoogle Scholar
  3. 3.
    Camps-Valls, G., Muñoz-Marí, J., Gómez-Chova, L.: Biophysical Parameter Estimation With a Semisupervised Support Vector Machine. IEEE Geosciences and Remote Sensing Letters 6, 248–252 (2009)CrossRefGoogle Scholar
  4. 4.
    Zhao, L., Jing, S., Butts, K.: Linear Programming SVM-ARMA With Application in Engine System Identification. IEEE Transaction on Automation Science and Engineering 8, 846–854 (2011)CrossRefGoogle Scholar
  5. 5.
    Zhong, L., Pan, H.: Pattern recognition. Wuhan University Press, Wuhan (2006)Google Scholar
  6. 6.
    Ali, F.A., Selvan, K.T.: A Study of PSO and its Variants in respect of Microstrip Antenna Feed Point optimization, vol. 8, pp. 1817–1820 (2009)Google Scholar
  7. 7.
    Fernandez-Martinez, J.L., Garcia-Gonzalo, E.: Stochastic Stability Analysis of the Linear Continuous and Discrete PSO Models. IEEE Transactions on Evolutionary Computation 15, 405–423 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Jiao Lan
    • 1
  • DianJun Li
    • 1
  • XiZhong Qin
    • 1
  • ZhenHong Jia
    • 1
  • Li Chen
    • 2
  • Lei Sheng
    • 2
  • Hong Li
    • 2
  1. 1.School of Information Science and EngineeringXinjiang UniversityUrumqiChina
  2. 2.China Mobile Group Xinjiang Company LimitedUrumqiChina

Personalised recommendations