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| 基于SAX的时间序列相似性度量方法* | |
| 引用本文： | 李桂玲,王元珍,杨林权,吴湘宁a.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. |
| 作者姓名： | 李桂玲 王元珍 杨林权 吴湘宁a |
| 作者单位： | 1. 中国地质大学计算机学院;华中科技大学计算机科学与技术学院,武汉430074 2. 华中科技大学计算机科学与技术学院,武汉,430074 3. 中国地质大学信息工程学院,武汉,430074 4. 中国地质大学计算机学院 |
| 基金项目： | 湖北省自然科学基金资助项目（2009CDB226）；中央高校基本科研业务费专项资金资助项目（CUGL100243) |
| 摘 要： | 符号化表示是一种有效的时间序列降维技术,其相似性度量是诸多挖掘任务的基础。基于SAX(sym-bolic aggregate approximation)的距离MINDIST_PAA_iSAX不满足对称性,在时间序列挖掘中具有局限性,提出了对称的度量Sym_PAA_SAX,且下界于欧拉距离。在真实数据集和合成数据集上的实验说明下界紧密性较好,相似搜索错报率较低。 |
| 关 键 词： | 时间序列 降维 相似性度量 下界 |
Research on similarity measure for time series based on SAX |
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| LI Gui-ling,WANG Yuan-zhen,YANG Lin-quan,WU Xiang-ninga.Research on similarity measure for time series based on SAX[J].Application Research of Computers,2012,29(3):893-896. | |
| Authors: | LI Gui-ling WANG Yuan-zhen YANG Lin-quan WU Xiang-ninga |
| Affiliation: | (1.a.School of Computer Science, b. School of Information Engineering, China University of Geosciences, Wuhan 430074,China; 2.School of Computer Science & Technology, Huazhong University of Science & Technology, Wuhan 430074,China) |
| 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. |
| Keywords: | time series dimensionality reduction similarity measure lower bounding |
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