[1]陈孝全[][],刘波[]. 基于支持向量机粒化的证券指数预测[J].计算机技术与发展,2015,25(04):148-152.
　CHEN Xiao-quan[] [],LIU Bo[]. Stock Index Prediction Based on Support Vector Machines and Information Granulation[J].,2015,25(04):148-152.
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 基于支持向量机粒化的证券指数预测()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年04期
页码:
148-152
栏目:
应用开发研究
出版日期:
2015-04-10

文章信息/Info

Title:
 Stock Index Prediction Based on Support Vector Machines and Information Granulation
文章编号:
1673-629X（2015)04-0148-05
作者:
 陈孝全[1][2];  刘波[1]
 1.华南师范大学 计算机学院；2. 深圳博物馆网络中心
Author(s):
 CHEN Xiao-quan[1] [2]; LIU Bo[1]
关键词:
 ; 股票指数; 近似支持向量机; 模糊信息粒化; 交叉验证; 回归分析
Keywords:
 ; stock index; Proximal Support Vector Machine ( PSVM) ; fuzzy information granulation; cross validation;  regression analysis
分类号:
TP301
文献标志码:
A
摘要:
 为分析股票价格指数变化，文中提出一种采用近似支持向量机( PSVM)将金融时间序列数据进行模糊信息粒化的方法，并用此方法对上证指数数据进行回归分析预测。其实现过程是以2008年到2013年的上证综指数据建立抛物型模糊粒子，运用近似支持向量机原理，采用交叉验证的方法对相关参数进行寻优，用优化参数对时间序列进行训练，并回归预测模糊粒子的三个参数来确定上证综指的走势变化。对于非线性难预测的股票指数，实验分析比较了实际数据与预测数据，证明具有较好的预测效果。
Abstract:
 To analyze the changes of the stock index,put forward a financial data time series prediction method based on proximal support vector machine and fuzzy information granulation,and this method is used for the Shanghai composite index data regression analysis and forecast. The parabolic fuzzy particle model is established about the Shanghai composite index data set between 2008 and 2013. Use the theory of proximal support vector machine combined cross validation method to optimize the parameters,train the time series data with the optimized parameters,and three parameter of fuzzy particles regression prediction determine the variation trend of the Shanghai composite index. The stock index is nonlinear and hard to predict,the experiment analyzes and compares both the actual data and the predicted data, which proves that it has good prediction effect.

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更新日期/Last Update: 2015-06-05