2008, Volume 10, Issue 11
Strategic Study of CAE >> 2008, Volume 10, Issue 11
Generalization and application in time series forecasting of the least square support vector machine method
School of Management, Fuzhou University, Fuzhou 350002, China
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Abstract
According to the theory that the present data contains more future information than historical data in time-series,the paper extends the prediction method of least square support vector machine and obtains a more general prediction model of least square support vector machine,and develops algorithm of the extended prediction model.Prediction examples of two time-series show that the extended model is more effective.Therefore it improves the value of the prediction method of least square support vector machine.
Keywords
least square support vector machine ; generalization ; time series ; forecasting
References
[ 1 ] 陈磊,张土乔.基于最小二乘支持向量机的时用水量预测模型[J].哈尔滨工业大学学报,2006,38(9):1528-1530 link1
[ 2 ] 韩敏.混沌时间序列预测理论与方法[M].北京:中国水利水电出版社,2007,208-209
[ 3 ] 吕金虎,陆君安,陈士华.混沌时间序列分析及其应用[M].武汉:武汉大学出版社,2002.14-18
[ 4 ] 施燕杰.基于支持向量机(SVM)的股市预测方法[J].统计与决策,2005,(4):123-125 link1
[ 5 ] 田翔,邓飞其.精确在线支持向量回归在股指预测中的应用[J].计算机工程,2005,31(22):18-20 link1
[ 6 ] 周万隆,姚艳.支持向量机在股票价格短期预测中的应用[J].商业研究,2006,(6):160-162 link1
[ 7 ] 王彦峰,高风.基于支持向量机的股市预测[J].计算机仿真,2006,23,(11):256-258 link1
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