Approach for effective fractal-based similarity search of stochastic non-stationary time series
一种基于分形和相似性查找的非平稳时间序列符号化表示法

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. A high-precision random non-stationary time series method named FSPA was proposed based on fractal theory and R/S analysis, which retained a non-linear time series and important fractal characteristics, and realized the reduction of the dimensions. The experiments have been performed on synthetic, as well as real data sequences to evaluate the proposed method, and the results indicate that the method has higher accuracy and requires less storage space.