Abstract
This study combines wavelet-based feature extractions with kernel partial least square (PLS) regression for international stock index forecasting. Wavelet analysis is utilized as a preprocessing step to decompose and extract most important time scale features from high dimensional input data. Owing to the high dimensionality and heavy multi-collinearity of the input data, a kernel PLS regression model is employed to create the most efficient subspace that keeping maximum covariance between inputs and outputs, and perform the final forecasting. Compared with neural networks, pure SVMs or traditional GARCH models, the proposed model performs best. The root-mean-squared forecasting errors are significantly reduced.
| Original language | English |
|---|---|
| Title of host publication | 2008 International Joint Conference on Neural Networks, IJCNN 2008 |
| Pages | 1910-1916 |
| Number of pages | 7 |
| DOIs | |
| Publication status | Published - 2008 Nov 24 |
| Event | 2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China Duration: 2008 Jun 1 → 2008 Jun 8 |
Other
| Other | 2008 International Joint Conference on Neural Networks, IJCNN 2008 |
|---|---|
| Country | China |
| City | Hong Kong |
| Period | 08-06-01 → 08-06-08 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Software
- Artificial Intelligence
Cite this
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Forecasting stock indices with wavelet-based kernel partial least square regressions. / Huang, Shian-Chang; Wu, Tung-Kuang.
2008 International Joint Conference on Neural Networks, IJCNN 2008. 2008. p. 1910-1916 4634059.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Forecasting stock indices with wavelet-based kernel partial least square regressions
AU - Huang, Shian-Chang
AU - Wu, Tung-Kuang
PY - 2008/11/24
Y1 - 2008/11/24
N2 - This study combines wavelet-based feature extractions with kernel partial least square (PLS) regression for international stock index forecasting. Wavelet analysis is utilized as a preprocessing step to decompose and extract most important time scale features from high dimensional input data. Owing to the high dimensionality and heavy multi-collinearity of the input data, a kernel PLS regression model is employed to create the most efficient subspace that keeping maximum covariance between inputs and outputs, and perform the final forecasting. Compared with neural networks, pure SVMs or traditional GARCH models, the proposed model performs best. The root-mean-squared forecasting errors are significantly reduced.
AB - This study combines wavelet-based feature extractions with kernel partial least square (PLS) regression for international stock index forecasting. Wavelet analysis is utilized as a preprocessing step to decompose and extract most important time scale features from high dimensional input data. Owing to the high dimensionality and heavy multi-collinearity of the input data, a kernel PLS regression model is employed to create the most efficient subspace that keeping maximum covariance between inputs and outputs, and perform the final forecasting. Compared with neural networks, pure SVMs or traditional GARCH models, the proposed model performs best. The root-mean-squared forecasting errors are significantly reduced.
UR - http://www.scopus.com/inward/record.url?scp=56349157584&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=56349157584&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2008.4634059
DO - 10.1109/IJCNN.2008.4634059
M3 - Conference contribution
SN - 9781424418213
SP - 1910
EP - 1916
BT - 2008 International Joint Conference on Neural Networks, IJCNN 2008
ER -