Time series online prediction algorithm based on least squares support vector machine

  • Wu Qiong  (吴琼)Email author
  • Liu Wen-ying  (刘文颖)
  • Yang Yi-han  (杨以涵)


Deficiencies of applying the traditional least squares support vector machine (LS-SVM) to time series online prediction were specified. According to the kernel function matrix’s property and using the recursive calculation of block matrix, a new time series online prediction algorithm based on improved LS-SVM was proposed. The historical training results were fully utilized and the computing speed of LS-SVM was enhanced. Then, the improved algorithm was applied to time series online prediction. Based on the operational data provided by the Northwest Power Grid of China, the method was used in the transient stability prediction of electric power system. The results show that, compared with the calculation time of the traditional LS-SVM(75-1 600 ms), that of the proposed method in different time windows is 40–60 ms, and the prediction accuracy(normalized root mean squared error) of the proposed method is above 0.8. So the improved method is better than the traditional LS-SVM and more suitable for time series online prediction.

Key words

time series prediction machine learning support vector machine statistical learning theory 


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Copyright information

© Published by: Central South University Press, Sole distributor outside Mainland China: Springer 2007

Authors and Affiliations

  • Wu Qiong  (吴琼)
    • 1
    Email author
  • Liu Wen-ying  (刘文颖)
    • 1
  • Yang Yi-han  (杨以涵)
    • 1
  1. 1.Key Laboratory of Power System Protection and Dynamic Security Monitory and Control of Ministry of EducationNorth China Electric Power UniversityBeijingChina

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