A high-precision approach for effective fractal-based similarity search of stochastic non-stationary time series

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.