Time series prediction using LS-SVM with particle swarm optimization

  • Authors:

  • Xiaodong Wang;Haoran Zhang;Changjiang Zhang;Xiushan Cai;Jinshan Wang;Meiying Ye

  • Affiliations:

  • College of Information Science and Engineering, Zhejiang Normal University, Jinhua, P.R. China;College of Information Science and Engineering, Zhejiang Normal University, Jinhua, P.R. China;College of Information Science and Engineering, Zhejiang Normal University, Jinhua, P.R. China;College of Information Science and Engineering, Zhejiang Normal University, Jinhua, P.R. China;College of Information Science and Engineering, Zhejiang Normal University, Jinhua, P.R. China;College of Mathematics and Physics, Zhejiang Normal University, Jinhua, P.R. China

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
  • Year:
  • 2006

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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.