%0 Journal Article
%T Approach for effective fractal-based similarity search of stochastic non-stationary time series<br>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
%A SUN Mei-yu
%A FANG Jia-nan
%A JIANG Xue-bo
%A YU Dong-mei
%A ZHOU Yu-ping
%A <br>孙梅玉
%A 方建安
%A 姜学波
%A 于冬梅
%A 周豫苹
%J 计算机应用
%D 2008
%I 
%X 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.
%K time series
%K fractal theory
%K symbolic representation
%K similarity search<br>时间序列
%K 分形理论
%K 符号化表示
%K 相似性查找
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=3A0C56BCBEE9D23A321A2647509937F7&yid=67289AFF6305E306&vid=D3E34374A0D77D7F&iid=B31275AF3241DB2D&sid=0407E07CB2FA770D&eid=8EAB8D766913D934&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=19