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)


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.


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



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).


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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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