Study on Prediction of Mackey-Glass Chaotic Time Series Using Support Vector Machines

Ya Li Ning; Xin You Wang; Xi Ping He

doi:10.4028/www.scientific.net/AMR.1051.1009

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 1051Study on Prediction of Mackey-Glass Chaotic Time...

Study on Prediction of Mackey-Glass Chaotic Time Series Using Support Vector Machines

Abstract:

Support Vector Machines (SVM), which is a new generation learning method based on advances in statistical learning theory, is characterized by the use of many standard technologies of machine learning such as maximal margin hyperplane, Mercel kernels and the quadratic programming. Because the best performance is obtained in many currently challenging applications, SVM has sustained wide attention, and has been become the standard tools of machine learning and data mining. But as a developing technology, SVM still have some problems and its applications are limited. In this paper, SVM and its applications in chaotic time series including predicting chaotic time series, focus on comparison in regression type selection, and kernel type selection in the same regression machine type.

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Periodical:

Advanced Materials Research (Volume 1051)

Pages:

1009-1015

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1051.1009

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Online since:

October 2014

Authors:

Ya Li Ning, Xin You Wang, Xi Ping He

Keywords:

Chaotic Time Series , Prediction, Support Vector Machine (SVM)

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