| URN |
etd-0805109-121651 |
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| Author |
Chi-wei Huang |
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| Author's Email Address |
kiwi@water.ee.nsysu.edu.tw |
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| Statistics |
This thesis had been viewed 5601 times. Download 3368 times. |
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| Department |
Electrical Engineering |
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| Year |
2008 |
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| Semester |
2 |
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| Degree |
Master |
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| Type of Document |
|
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| Language |
zh-TW.Big5 Chinese |
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| Title |
A Multiple-Kernel Support Vector Regression
Approach for Stock Market Price Forecasting |
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| Date of Defense |
2009-07-22 |
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| Page Count |
60 |
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| Keyword |
SMO
multiple-kernel learning
support vector regression
Stock market forecasting
gradient projection
|
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| Abstract |
Support vector regression has been applied to stock market forecasting problems. However, it is usually needed to tune manually the hyperparameters of the kernel functions. Multiple-kernel learning was developed to deal with this problem, by which the kernel matrix weights and Lagrange multipliers can be simultaneously derived through semidefinite programming. However, the amount of time and space required is very demanding. We develop a two-stage multiple-kernel learning algorithm by incorporating sequential minimal optimization and the gradient projection method.
By this algorithm, advantages from different hyperparameter settings can be combined and overall system performance can be improved. Besides, the user need not specify the hyperparameter settings in advance, and trial-and-error for determining appropriate hyperparameter settings can then be avoided. Experimental results, obtained by running on datasets taken from Taiwan Capitalization Weighted Stock Index, show that our method performs better than other methods. |
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| Advisory Committee |
Tzung-Pei Hong - chair
Wen-Yang Lin - co-chair
Chaur-Heh Hsieh - co-chair
Chung-Ming Kuo - co-chair
Shie-Jue Lee - advisor
|
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| Files |
indicate access worldwide |
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| Date of Submission |
2009-08-05 |
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