NONLINEAR MODELING FOR TIME SERIES BASED ON THE GENETIC PROGRAMMING AND ITS APPLICATIONS
(0) This paper deals with clustering of segments of stock prices by using nonlinear modeling system for time series based on the Genetic Programming (GP). We apply the GP procedure in learning phase of the system where we improve the nonlinear functional forms to approximate the models used to generate time series. The variation of the individuals with relatively high capability in the pool can cope with clustering for various kinds of time series which belong to the same cluster similar to the classifier systems. As an application,we show clustering of artificially generated time series obtained by expanding or shrinking by transformation functions. Then, we apply the system to clustering of 8 kinds of segments of real stock prices.
Nonlinear modeling Genetic Programming time series Clustering
JIAN-JUN LU YUN-LING LIU SHOZO TOKINAGA
Graduate School of Economics, Kyushu University, Fukuoka 812-8581, Japan College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, Ch
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
2097-2102
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）