A HIGH-PRECISION APPROACH FOR EFFECTIVE FRACTAL-BASED SIMILARITY SEARCH OF STOCHASTIC NON-STATIONARY TIME SERIES

摘要：

Dozens of high level representations of time series have been introduced for data mining in the literature. Traditional dimension reduction methods about similarity query introduce the smoothness to data series in some degree that the important features of time series about non-linearity and fractal are destroyed. In this paper a high-precision approach based on fractal theory and R/S analysis are proposed. The representation is unique in which it allows dimensionality reduction and it also preserved the fractal features. The experiments have been performed on synthetic, as well as real data sequences to evaluate the proposed method.

关键词： Time Series Fractal Theory Symbolic Representation Similarity Search

作者: MEI-YU SUN

作者单位: College of Information Science and technology, Donghua University.Shanghai 201620, China Computer Department.Shandong Labour Union Administrators college, Jinan 250100, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 136-141

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）