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Time Series Prediction Using LS-SVM with Particle Swarm Optimization

  • Xiaodong Wang
  • Haoran Zhang
  • Changjiang Zhang
  • Xiushan Cai
  • Jinshan Wang
  • Meiying Ye
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

Time series analysis is an important and complex problem in machine learning. In this paper, least squares support vector machine (LS-SVM) combined with particle swarm optimization (PSO) is used to time series prediction. The LS-SVM can overcome some shortcoming in the multilayer perceptron (MLP) and the PSO is used to tune the LS-SVM parameters automatically. A benchmark problem, Hénon map time series, has been used as an example for demonstration. It is showed this approach can escape from the blindness of man-made choice of the LS-SVM parameters. It enhances the efficiency and the capability of prediction.

Keywords

Particle Swarm Optimization Little Square Support Vector Machine Time Series Prediction Little Square Support Vector Machine Model Little Square Support Vector Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiaodong Wang
    • 1
  • Haoran Zhang
    • 1
  • Changjiang Zhang
    • 1
  • Xiushan Cai
    • 1
  • Jinshan Wang
    • 1
  • Meiying Ye
    • 2
  1. 1.College of Information Science and EngineeringZhejiang Normal UniversityJinhuaP.R. China
  2. 2.College of Mathematics and PhysicsZhejiang Normal UniversityJinhuaP.R. China

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