Application of TWSVR Models in Stock Price Forecast-Reference-Cited by-同舟云学术

Application of TWSVR Models in Stock Price Forecast

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence - RICAI 2019
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Author:

Cui Haofeng¹,Yin Xiangfeng²,Wen Xueting¹

Affiliation:

1. School of mathematics and computational sciences, Hunan university of science and technology, Xiangtan, Hunan province, China

2. School of mathematics and computational sciences, Hunan university of science and technology and Hunan Engineering Research Center for Big Data and Intelligent Decision-making of Industrial Development, Xiangtan, Hunan province, China and Hunan Engineering Research Center for Big Data and Intelligent Decision-making of Industrial Development, Xiangtan, China

Publisher

ACM Press

Reference13 articles.

1. B.M. Henrique, V.A. Sobreiro, H. Kimura (2018). Stock price prediction using support vector regression on daily and up to the minute prices. The Journal of Finance and Data Science, 4, 183--201.

2. W. Wang, Z. Yuan, W. Xie and J. Yang (2009). Association Rule Study on Combination of Stock K Line. Journal of Chengdu University (Natural Science Edition), 28, 268--271.

3. W. Zheng (2014). Short-Term Forecast of Stock Price of Shanghai Composite Index Based on ARIMA Model. Economic Research Guide, 234, 136--137.

4. K.J. Kim (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55 (1), 307--319.

5. J.C. Chen, H.Z. Chen, Y.J. Huo and W.T. Gao (2017). Application of SVR Models in Stock Index Forecast Based on Different Parameter Search Methods. Open Journal of Statistics, 7, 194--202.

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