2012 ©
             Publication
Journal Publication
Research Title Application of Singular Spectrum Analysis and Kernel-based Extreme Learning Machine for Stock Price Prediction 
Date of Distribution 20 September 2016 
Conference
     Title of the Conference International Joint Conference on Computer Science and Software Engineering (JCSSE) 2016 
     Organiser มหาวิทยาลัยขอนแก่น 
     Conference Place โรงแรมพูลแมน ราชาออร์คิด 
     Province/State ขอนแก่น 
     Conference Date 13 July 2016 
     To 15 July 2016 
Proceeding Paper
     Volume
     Issue
     Page
     Editors/edition/publisher IEEE 
     Abstract Stock prediction is known as one of the most challenging task in the field of time series analysis. Many existing studies emphasize only in improving accuracy, but not many are concerned about improving speed of the prediction. This paper proposes a stock prediction model using kernel-based extreme learning machine with singular spectrum analysis (SSA-KELM) as a preprocessing tool. The prediction performance of SSA-KELM is compared to five other models. Three stock price data are used to evaluate the performance. The experimental results show that all SSA-based models can outperform non-SSA models. The results are also shown that SSA-KELM can achieve the highest accuracy and the lowest training time among other SSA-based models. The proposed model can therefore be considered as an efficient model for stock price prediction. 
Author
565020224-2 Mr. PREUK SUKSIRI [Main Author]
Science Master's Degree

Peer Review Status มีผู้ประเมินอิสระ 
Level of Conference นานาชาติ 
Type of Proceeding Full paper 
Type of Presentation Oral 
Part of thesis true 
Presentation awarding false 
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