NDLI: Forecasting share price using wavelet transform and LS-SVM based on chaos theory

Content Provider	IEEE Xplore Digital Library
Author	Jianguo Zhou Tao Bai Aiguang Zhang Jiming Tian
Copyright Year	2008
Description	Author affiliation: Sch. of Bus. Adm., North China Electr. Power Univ., Baoding (Jianguo Zhou; Tao Bai; Aiguang Zhang; Jiming Tian)
Abstract	In the analysis of predicting share price based on least squares support vector machine (LS-SVM), the instability of the time series could lead to decrease of prediction accuracy. On the other hand, three SVM parameters, c, epsiv and sigma, must be carefully predetermined in establishing an efficient LS-SVM model. In order to solve the problems mentioned above, in this paper, the hybrid of wavelet transform (WT) with LS-SVM model was established. First the chaotic feature of share price is verified with chaos theory. It can be seen that share price possessed chaotic features, providing a basis for performing short-term forecast of share price with the help of chaos theory. Average mutual information (AMI) method is used to find the optimal time lag. Then the time series is decomposed by wavelet transform to eliminate the instability. Caopsilas method is adopted to determine free parameters of support vector machines. Additionally, the proposed model was tested on the prediction of share price of one listed company in China. Especially, In order to validate the rationality of chosen dimension, the other three random dimensions were selected to compare with the calculated dimension. And to prove the effectiveness of the model, PSVM algorithm was used to compare with the result of WT-SVM. Experimental results showed that the proposed model performed the best predictive accuracy and generalization, implying that integrating the wavelet transform with LS-SVM model can serve as a promising alternative for share price prediction.
Starting Page	300
Ending Page	304
File Size	588744
Page Count	5
File Format	PDF
ISBN	9781424416738
DOI	10.1109/ICCIS.2008.4670953
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Share prices Wavelet transforms Chaos Support vector machines Predictive models Accuracy Time series analysis Least squares methods Mutual information Ambient intelligence LS-SVM Share Price Forecast Chaotic Time Series Wavelet Transform
Content Type	Text
Resource Type	Article

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