基于优化GA属性约简的上证指数预测  

Shanghai Composite Index Forecasting Based on Optimized GA Attribute Reduction

作　　者：严晓明[1] 

机构地区：[1]福建师范大学数学与计算机科学学院,福建福州350108

出　　处：《福建师范大学学报（自然科学版）》2011年第5期29-33,共5页Journal of Fujian Normal University：Natural Science Edition

基　　金：福建省自然科学基金资助项目(2009J01273)

摘　　要：结合粗糙集的相关理论,优化了GA属性约简方法,针对上证指数预测的具体问题,对遗传算法的初始种群和适应度函数进行改进,将上证指数10年间数据的58个属性构成的训练集进行属性约简,并应用参数优化后的SVM分别以属性约简前后的数据集对开盘指数进行回归预测.仿真结果表明,用该算法进行属性约简后,原始数据集中冗余属性对预测结果的影响下降,预测精度提高,建模时间也相应的减少,得到了较好的结果.An optimized genetic algorithm for attribute conduction which was based on rough set theories was proposed. Initial population and fitness function of the genetic algorithm were improved according to situation of Shanghai Composite Index forecasting. It performs application of attribute reduction with training set which retrieve 58 attributes data from Shanghai Composite Index in recent ten years. Then, it conducts regression prediction by using parameters optimized SVM to predict stock index on the original dataset and the simplified dataset. Emulation experiment results show that it has better predict precision and time consuming performance by using simplified dataset.

关 键 词：遗传算法 属性约简 上证指数 SVM 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]