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Multivariate Time Series Prediction Based on Multi-Output Support Vector Regression

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 214)

Abstract

An improved support vector regression (SVR) model is presented in this paper and the model can train and predict both the multiple input and output samples, which can avoid SVR’s modeling for each output individually when predicting the multivariate time series. The proposed multi-output SVR (MOSVR) model can guarantee the regression ability of each output by choosing different kernel functions and model parameters for different outputs of one single optimization problem. On this basis, the norm summation of regression weight vector, the error summation of each output and the total error are minimized so as to be sure that the MOSVR model satisfies the structure risk minimization (SRM) principle. The experimental results based on several multivariable time series show that the MOSVR has better adaptability and gains less total regression error than the SVR.

Keywords

Support vector regression Multi-output SVR Multivariate time series Time series prediction 

Notes

Acknowledgments

This work was jointly supported by the National Natural Science Foundation for Young Scientists of China (Grant No: 61202332) and China Postdoctoral Science Foundation (Grant No: 2012M521905).

References

  1. 1.
    Yoon H, Yang K, Cyrus S (2005) Feature subset selection and feature ranking for multivariate time series. IEEE Trans Knowl Data Eng 17(9):1186–1198CrossRefGoogle Scholar
  2. 2.
    Han M, Fan MM (2006) Application of neural on multivariate time series modeling and prediction. In: Proceedings of the 2006 American control conference, vol 7. Minneapolis, Minnesota, USA, pp 3698–3703Google Scholar
  3. 3.
    Tsang, S., Kao, B., Yip, K.Y.: Decision trees for uncertain data. In: Proceedings of the 2009 IEEE international conference on data engineering. Washington, DC: IEEE Computer Society, pp 441–444Google Scholar
  4. 4.
    Yang K, Shahabi C (2004) A PCA-based similarity measure for multivariate time series. In: Proceedings of the 2nd ACM international workshop on multimedia databases. New York, ACM Press, pp 65–74Google Scholar
  5. 5.
    Aboy M, Márquez OW, McNames J, Hornero R, Trong T, Goldstrein B (2005) Adaptive modeling and spectral estimation of nonstationary biomedical signals based on Kalman filtering. IEEE Trans Biomed Eng 52(8):1485–1489CrossRefGoogle Scholar
  6. 6.
    Mukhopadhyay N, Chatterjee S (2007) Causality and pathway search in microarray time series experiment. Bioinformatics 23:442–449CrossRefGoogle Scholar
  7. 7.
    Fieuws S, Verbeke G, Molenberghs G (2007) Random-effects models for multivariate repeated measures. Stat Methods Med Res 16(5):387–397MathSciNetCrossRefGoogle Scholar
  8. 8.
    Yang K, Shahabi C (2007) An efficient k-nearest neighbor search for multivariate time series. Inf Comput 205(1):65–98MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Guan HS, Jiang QS (2009) Pattern matching method based on point distribution for multivariate time series. J Softw 20(1): 67–79 (In Chinese)Google Scholar
  10. 10.
    Wu CH, Ho JM, Lee DT (2004) Travel-time prediction with support vector regression. IEEE Trans Intell Transp Syst 5(4):276–281CrossRefGoogle Scholar
  11. 11.
    Cao LJ, Francis EHT (2003) Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans Neural Netw 14(6):1506–1518CrossRefGoogle Scholar
  12. 12.
    Tang FM (2005) Research on support vector machine algorithms based on statistical learning theory. PhD Dissertation, Huazhong University of Science and TechnologyGoogle Scholar
  13. 13.
    Bi JB (2003) Support vector regression with application in automated drug discovery. Rensselaer Polytechnic Institute, Troy, New York, pp 87–92Google Scholar
  14. 14.
    Ren R, Xu J, Zhu SH (2006) Prediction of chaotic time sequence using least squares support vector domain. Acta Physica Sinica 55(2): 555–563 (In Chinese)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Xi’an Research Institute of Hi-TechShaanxiChina
  2. 2.Air Force Engineering UniversityShaanxiChina

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