文化算法在股票数据建模中的应用  

Application of Cultural Algorithms in Stock Data Modeling

作　　者：颜雪松[1] 姜韬[1] 韩增新[1] 窦明罡[1] 

机构地区：[1]中国地质大学(武汉)计算机学院,湖北武汉430074

出　　处：《计算机工程与科学》2010年第6期142-145,共4页Computer Engineering & Science

基　　金：中央高校基本科研业务费专项资金资助项目(CUGL090245)

摘　　要：数据预测在金融投资领域占有重要地位,而股票价格是金融数据中最复杂的数据类型之一。本文将文化算法应用于股票指数建立时间序列模型,并且针对股票数据的特点,选择适应股票规律的文化算法模型,并与线性回归算法得到的数据结果进行了对比分析,以此来分析文化算法在该问题应用上的优劣。实验结果表明,利用文化算法模型进行预测取得了较好的结果,其预测精度较高,预测的相对误差在3%以内,涨跌分析正确率在70%以上。Data forecast is important in the field of economic investment, and the price of the stock is one of the most the complex data types in economic data. This paper uses cultural algorithms to establish time series models, focuses on the characteristics of the stock data to select a cultural algorihtm model which is fit for the stock rules, and comparies with the result which is obtained by linear regression algorithms, in order to analyse the merits of the application of cultural algorithms for the problem. The result shows that using cultural algorithms models for forecasting achieves better results, and the forecast accuracy is high. The relative error of the forecast is within 3%, and the rate of correct analysis is more than 70%.

关 键 词：文化算法 演化计算 数据预测 股票数据 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]