摘要
金融时间序列分析与预测作为金融领域重要研究方向,对于揭示市场动态、指导投资决策以及维护金融稳定具有关键意义.然而,金融时间序列数据具有复杂性、高噪声等特点,使得传统聚类方法在处理这些问题上往往存在局限性.粗糙聚类作为一种基于粗糙集理论的方法,具有处理上述问题的潜力.首先介绍粗糙集理论及粗糙聚类方法的基本概念和原理.然后,重点关注粗糙聚类在金融时间序列分析与预测领域的应用,包括宏观经济预测、股票市场分析等.最后通过实际应用实例,展示粗糙聚类在金融时间序列分析与预测中的重要价值.
Abstract
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.
基金项目
河北省高等学校科学技术研究项目(QN2019338)
沧州市重点研发计划自筹项目(204102008)
天津天狮学院教学改革与研究项目(J22004)
NETL
NSTL
维普
万方数据