基于遗传算法和模糊决策树的时间序列预测模型  

Model of time series forecasting based on GA and fuzzy decision tree

作　　者：刘利[1] 何先平[2] 

机构地区：[1]池州学院数学计算机系,安徽池州247000 [2]长江大学信息与数学学院,湖北荆州434023

出　　处：《计算机工程与设计》2008年第19期5044-5046,共3页Computer Engineering and Design

基　　金：安徽省池州学院校级基金项目(XYK200606)

摘　　要：股票价格预测总是投资者和技术分析者感兴趣的一个主题。然而,决定买卖股票的最好时间仍然是困难的,因为有很多因素可能影响股票价格。通过改进模糊决策树建立了一个新型金融时间序列数据预测模型。该预测模型融合数据聚类技术,模糊决策树及遗传算法来构建基于历史数据和技术指标的一个决策系统。提出的GAFDT模型在与各种股票的其它方法相比较时有平均预测准确率为0.82的最好绩效。Stock price predictions have always been a subject of interest for investors and professional analysts. Nevertheless, determining the best time to buy or sell a stock remains very difficult because there are many factors that may influence the stock prices. A novel financial time series forecasting model is established by evolving and clustering fuzzy decision tree for stocks. This forecasting model integrates a data clustering technique, a fuzzy decision tree （FDT）, and genetic algorithms （GA） to construct a decision-making system based on historical data and technical indexes. The proposed GAFDT model has the best performance of 82% average hit rate when com- pared with other approaches on various stocks.

关 键 词：模糊理论 决策树 逐步回归 股票价格预测 遗传算法 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]