Proceedings of the 3rd International Conference on Mechatronics, Robotics and Automation

The Wavelet Transform with best decomposition Level and Relevant Vector Machine Based Approach for Chaotic Time Series Forecasting

Authors
Xiao-Lu Wang, Jian Liu, Jian-Jun Lu
Corresponding Author
Xiao-Lu Wang
Available Online April 2015.
DOI
https://doi.org/10.2991/icmra-15.2015.184How to use a DOI?
Keywords
chaotic time series; phase space reconstruction; wavelet transform; RVM; forecasting
Abstract
In order to accurately predict the chaotic time series, a novel approach based on integration of wavelet transform and Relevant Vector Machine (RVM) is proposed. The best wavelet decomposition level is determined with the condition that a certain function space orthogonal projection energy in wavelet MRA, is smaller than the largest energy of the forecasting biases. Delay mapping is introduced to transform the different components into new samples of historical characteristics, after wavelet transform. The different new samples are predicted by their corresponding forecasters, respectively. The final forecasting result is obtained by combining all the predicted results. The sparse relevant support vector and its corresponding hyper parameters are calculated on the new sample space of time series by the Sparse Bayesian learning process. Based on which the prediction results are work out. The results show that the approach only using the SVM or RVM based forecaster the averaged prediction biases is more than 10%. The tracking ability and the dynamic behavior are remarkably improved to the averaged biases of 5.43% by using the wavelet transform with best decomposition Series and RVM based forecaster. It is indicated that the suggested approach is feasible and effective.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 3rd International Conference on Mechatronics, Robotics and Automation
Series
Advances in Computer Science Research
Publication Date
April 2015
ISBN
978-94-62520-76-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmra-15.2015.184How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xiao-Lu Wang
AU  - Jian Liu
AU  - Jian-Jun Lu
PY  - 2015/04
DA  - 2015/04
TI  - The Wavelet Transform with best decomposition Level and Relevant Vector Machine Based Approach for Chaotic Time Series Forecasting
BT  - Proceedings of the 3rd International Conference on Mechatronics, Robotics and Automation
PB  - Atlantis Press
SP  - 947
EP  - 953
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmra-15.2015.184
DO  - https://doi.org/10.2991/icmra-15.2015.184
ID  - Wang2015/04
ER  -