Abstract：This paper presents a new method of stock market time series data mining. It combines regularized neural network with rough set. The process includes preprocessing of time series and data mining. The preprocessing cleans and filters time series. Then, the time series are partitionel into a series of static patterns, which is based on the trend (i.e., increasing or decreasing) of closing price. The most important predicting attributes identified from every model form an information table. The regularized neural network is used to learn and predict the data. Rough set can extract rule knowledge in the neural network, which can be used to predict the time series’ behavior in the future. This method combines the generalization faculty of regularized neural network and the rule reduction capability of rough set. The experimental results demonstrate the effectiveness of the algorithm.
王晓晔; 王正欧. 正则化训练的神经网络与粗集理论相结合的股票时间序列数据挖掘技术[J]. 电子与信息学报, 2004, 26(4): 625-631 .
Wang Xiao-ye①②; Wang Zheng-ou①. Stock Market Time Series Data Mining Based on Regularized Neural Network and Rough Set. , 2004, 26(4): 625-631 .
2004, Vol. 26 
