计算机应用研究2012，Vol.29Issue(3)：893-896,4.DOI:10.3969/j.issn.1001-3695.2012.03.025
基于SAX的时间序列相似性度量方法
Research on similarity measure for time series based on SAX
摘要
Abstract
Symbolic approximation is an effective dimensionality reduction technique for time series, its similarity measure is a basis for various mining tasks. MINDIST_PAA_iSAX is a distance function based on symbolic aggregate approximation (SAX) , but it does not satisfy symmetry, so it has limitation in mining time series. This paper put forward and proved a symmetric distance measure Sym_PAA_SAX to be lower bounding to Euclidean distance. Experiments on real and synthetic data sets show its better tightness of lower bounding and lower false positives rate in similarity search.关键词
时间序列/降维/相似性度量/下界Key words
time series/ dimensionality reduction/ similarity measure/ lower bounding分类
信息技术与安全科学引用本文复制引用
李桂玲,王元珍,杨林权,吴湘宁..基于SAX的时间序列相似性度量方法[J].计算机应用研究,2012,29(3):893-896,4.基金项目
湖北省自然科学基金资助项目(2009CDB226) （2009CDB226）
中央高校基本科研业务费专项资金资助项目(CUGL100243) （CUGL100243）
