Research on low dimension fractal representation and similarity measure for stock indices time series
Wang Hongbo;Luo He;Peng Zhanglin;Wang Sufeng;School of Management, Hefei University of Technology;Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education;Management Department of Digital Bank,Bank of Nanjing;School of Management, Anhui Jianzhu University;
The low dimension fractal representation and similarity measure problem with complex volatility stock indices time series is studied by establishing a low dimension fractal representation on the basis of complex volatility tendency feature. A similarity measure method based on low dimension fractal representation is put forward to solve this problem. According to the characteristics of the problem, a tendency feature extraction technology based on dimension reduction is put forward which satisfies the demands of low dimension fractal representation for volatility tendency feature. Further, this paper establishes a similarity measure method considering complex volatility tendency feature to classify different types of stock indices time series. The results of the proposed method and other three similarity measure methods in solving several computational experiments with real data are compared, and the experimental results demonstrate the validity and the accuracy of the the proposed method.