TY - GEN
T1 - Support vector regression based approach for key index forecasting with applications
AU - Yin, Shen
AU - Wu, Fang
AU - Luo, Hao
AU - Gao, Huijun
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/28
Y1 - 2015/9/28
N2 - With the rapid development in science and technology, data acquisition, storage and mining technology are widely applied to various fields. All aspects of people's lives are recorded as data. Through the analyzing and arranging of data, people can get a lot of valuable information. In this paper, support vector machine (SVM), least squares support vector machine (LSSVM) and partial least squares (PLS) are respectively used in the field of economic research. Real-time monitoring and forecasting for stock index is vital to the market. The changing trend and index of stocks are predicted according to the analysis to the history data of the stock. By combining particle swarm algorithm (PSO) algorithm and LSSVM algorithm, the parameters in the LSSVM model can be optimized. These algorithms are compared on the basis of their forecasting results.
AB - With the rapid development in science and technology, data acquisition, storage and mining technology are widely applied to various fields. All aspects of people's lives are recorded as data. Through the analyzing and arranging of data, people can get a lot of valuable information. In this paper, support vector machine (SVM), least squares support vector machine (LSSVM) and partial least squares (PLS) are respectively used in the field of economic research. Real-time monitoring and forecasting for stock index is vital to the market. The changing trend and index of stocks are predicted according to the analysis to the history data of the stock. By combining particle swarm algorithm (PSO) algorithm and LSSVM algorithm, the parameters in the LSSVM model can be optimized. These algorithms are compared on the basis of their forecasting results.
KW - Least squares support vector machine
KW - Particle swarm algorithm
KW - Real-time monitoring
KW - Support vector machine regression
UR - https://www.scopus.com/pages/publications/84949514411
U2 - 10.1109/INDIN.2015.7281800
DO - 10.1109/INDIN.2015.7281800
M3 - 会议稿件
AN - SCOPUS:84949514411
T3 - Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015
SP - 591
EP - 596
BT - Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference on Industrial Informatics, INDIN 2015
Y2 - 22 July 2015 through 24 July 2015
ER -