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

传统的时间序列表示方法均在不同程度上采用了对数据的约简手段，从而破坏了时间序列的非线性和分形这些重要的本质特征，也就使得时间序列的相似性匹配误差加大。提出一种高精度的随机非平稳时间序列表示方法FSPA，该方法将分形理论和R/S方法应用到现有的时间序列表示方法中，既保留了时间序列的非线性和分形的重要特征，同时也实现了维度的约简。实验分别在合成数据和实际数据上进行，结果表明，该方法具有更高的精度且需要较少的存储空间。

Approach for effective fractal-based similarity search of stochastic non-stationary time series

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.