摘要
传统股票指数研究方法多停留在经验判断或简单的数据分析阶段,主要方法有基本面分析法、交易指标分析法等,这类分析方法或是对以往数据包含的信息使用效率比较低,或是对使用者的经验积累要求很高.近年来,数据挖掘方法在股市中已有很多成功的应用.在上述工作的基础上,从以下三方面提出一种改进的糊时间序列(fuzzy time series,FTS)模型并将其应用于股市预测中:一是提出了新的区间划分方法;二是提出新的模糊集权重公式;三是运用SVM分类算法进行模型修正,提出组合FTS模型.样本是选取1996~2003年上证指数数据,利用提出模型进行指数预测.实验结果表明,与多种重要FTS模型进行比较,本文提出的改进模型效果更优.
Traditional methods for stock index research are still at the stage of judging by experience or relying on simple data analysis,among which fundamental analysis and trading indicator analysis frequently used. These methods have noticeable disadvantages: Inefficient utilization of existing information or requirement for highly experienced users. A modified fuzzy time series( FTS) model was proposed based on the following three aspects.Firstly,a new method of interval division was developed. Secondly,a new weight formula for fuzzy set was devised. Thirdly,a modified FTS model was built with the application of SVM classification model. Predictions for stock index were made by using the proposed model. Experiment results from Shanghai index data ranging from1996 to 2003 indicate that compared with other important FTS models; the proposed model provides better performance.
基金
国家自然科学基金(60803055)
教育部人文社会科学一般项目(08JC630041)资助
关键词
模糊时间序列
SVM算法
股指预测
fuzzy time series
SVM algorithm
stock index prediction
