Paper Title:
A New Time Series Regression Method Based on Support Vector Machine Plus and Genetic Algorithm
  Abstract

Support vector machine (SVM) is gaining popularity on time series analysis due to its advanced theory foundation. The introduction of the hidden information on the basis of SVM is called support vector machine plus (SVM+). However, the hidden information which provides something closely associated with the time series increases the difficulty of training SVM model. In this paper, a new time series regression method GA-RSVM+ is put forward, in which Genetic Algorithm (GA) is used to search the optimal combination of free parameters. The experimental result shows that GA-RSVM+ can accurately determine the parameters on its own and achieve best regression precision. This method has a clear advantage in the regression analysis of time series.

  Info
Periodical
Advanced Materials Research (Volumes 201-203)
Edited by
Daoguo Yang, Tianlong Gu, Huaiying Zhou, Jianmin Zeng and Zhengyi Jiang
Pages
2277-2280
DOI
10.4028/www.scientific.net/AMR.201-203.2277
Citation
W. Sun, G. X. Meng, Q. Ye, J. Z. Zhang, L. W. Zhang, "A New Time Series Regression Method Based on Support Vector Machine Plus and Genetic Algorithm", Advanced Materials Research, Vols. 201-203, pp. 2277-2280, 2011
Online since
February 2011
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$35.00
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