Time series prediction using support vector machines, the orthogonal and the regularized orthogonal least-squares algorithms

2002 ◽  
Vol 33 (10) ◽  
pp. 811-821 ◽  
Author(s):  
K. L. Lee ◽  
S. A. Billings
Keyword(s):  
Time Series ◽  
Support Vector Machines ◽  
Least Squares ◽  
Time Series Prediction ◽  
Support Vector ◽  
Orthogonal Least Squares ◽  
Vector Machines

Financial time series prediction using least squares support vector machines within the evidence framework

2001 ◽  
Vol 12 (4) ◽  
pp. 809-821 ◽  
Author(s):  
T. Van Gestel ◽  
J.A.K. Suykens ◽  
D.-E. Baestaens ◽  
A. Lambrechts ◽  
G. Lanckriet ◽  
...  
Keyword(s):  
Time Series ◽  
Support Vector Machines ◽  
Least Squares ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Support Vector ◽  
Financial Time ◽  
Evidence Framework ◽  
Vector Machines

Study on Prediction of Chaotic Time Series Using Least Squares Support Vector Machines

2014 ◽  
Vol 1061-1062 ◽  
pp. 935-938
Author(s):  
Xin You Wang ◽  
Guo Fei Gao ◽  
Zhan Qu ◽  
Hai Feng Pu
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Least Squares ◽  
Prediction Accuracy ◽  
Chaotic Time Series ◽  
Support Vector ◽  
Vector Machines ◽  
Online Prediction ◽  
Better Than

The predictions of chaotic time series by applying the least squares support vector machine (LS-SVM), with comparison with the traditional-SVM and-SVM, were specified. The results show that, compared with the traditional SVM, the prediction accuracy of LS-SVM is better than the traditional SVM and more suitable for time series online prediction.

MODELING OF CHAOTIC SYSTEMS WITH MULTIWAVELET TRANSFORM COMBINED WITH RECURRENT LEAST SQUARES SUPPORT VECTOR MACHINES

2007 ◽  
Vol 05 (01) ◽  
pp. 1-13
Author(s):  
ZHENG XIANG ◽  
TAIYI ZHANG ◽  
JIANCHENG SUN
Keyword(s):  
Time Series ◽  
Support Vector Machines ◽  
Least Squares ◽  
Chaotic Systems ◽  
Support Vector ◽  
Vector Machines ◽  
Temporal Structures ◽  
Multiwavelet Transform ◽  
Original Time ◽  
Dynamic Invariants

A new algorithm for modeling of chaotic systems is presented in this paper. First, more information is acquired utilizing the reconstructed embedding phase space, and the multiwavelets transform provides a sensible decomposition of the data so that the underlying temporal structures of the original time series become more tractable. Second, based on the Recurrent Least Squares Support Vector Machines (RLS-SVM), modeling of the chaotic system is realized. To demonstrate the effectiveness of our algorithm, we use the power spectrum and dynamic invariants involving the Lyapunov exponents and the correlation dimension as criterions, and then apply our method to Chua's circuit time series. The similarity of dynamic invariants between the original and generated time series shows that the proposed method can capture the dynamics of the chaotic time series more effectively.

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