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A Multiple-Kernel Support Vector Regression Approach for Stock Market Price Forecasting

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 semideļ¬nite 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.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0805109-121651
Date05 August 2009
CreatorsHuang, Chi-wei
ContributorsChaur-Heh Hsieh, Chung-Ming Kuo, Wen-Yang Lin, Shie-Jue Lee, Tzung-Pei Hong
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageCholon
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0805109-121651
Rightsunrestricted, Copyright information available at source archive

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