The combining kernel PCA with PSO-SVM for chaotic time series prediction model

Chaotic time series analysis or forecasting is an important and complex problem in machine learning. As an effective tool, support vector machine (SVM) has been broadly adopted in pattern recognition and machine learning fields. In developing a successful SVM classifier, eliminating noise and extrac...

Full description

Saved in:
Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 1; pp. 467 - 472
Main Authors Qi-Song Chen, Xin Zhang, Shi-Huan Xiong, Xiao-Wei Chen
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Chaos
Feature extraction
Kernel
KPCA
Machine learning
Pattern recognition
Prediction model
Predictive models
Principal component analysis
PSO
Support vector machine classification
Support vector machines
SVM
Time series analysis
Online AccessGet full text

Cover

More Information
Summary:Chaotic time series analysis or forecasting is an important and complex problem in machine learning. As an effective tool, support vector machine (SVM) has been broadly adopted in pattern recognition and machine learning fields. In developing a successful SVM classifier, eliminating noise and extracting feature are very important. This paper proposes the application of kernel PCA to LS-SVM for feature extraction. Then PSO algorithm is employed to optimization of these parameters in LS-SVM. The novel chaotic time series analysis model integrates the advantages of wavelet transform, KPCA, PSO and LS-SVM. Compared with other predictors, this model has greater generality ability and higher accuracy.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212558