Abstract
Presents a method of stock market time series data mining, which combines a regularized neural network with rough sets. The process includes preprocessing of a time series database and data mining. The preprocessing cleans and filters time series. Then, we partition the time series into a series of static patterns, which is based on the trend (i.e., increasing or decreasing) of the closing price. An information table is formed by the most important predictable attributes and target attributes identified from each pattern. The regularized neural network (RNN) is used to study and predict the data. Rough sets can extract rule knowledge in the trained neural network that can be used to predict the time series behavior in the future. The method combines the high generalization faculty of the regularized neural network and the rule reduction capability of rough sets. An experiment demonstrates the effectiveness of the algorithm.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.