Rough Clustering of Financial Time Series in Data Mining
Financial time series analysis and prediction,as an important research direction in the financial field,are crucial for revealing market dynamics,guiding investment decisions,and maintaining financial stability.However,the complexity and high noise of financial time series data often limit traditional clustering methods in dealing with these issues.Rough clustering,as a method based on rough set theory,has the potential to address the aforementioned issues.Firstly,the basic concepts and principles of rough set theory and rough clustering methods are introduced.Then,we focus on the application of rough clustering in financial time series analysis and prediction,including macro economic forecasting,stock market analysis,etc.Finally,through practical application examples,the important value of rough clustering in financial time series analysis and prediction are shown.
NETL
NSTL
万方数据
维普
