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Wavelet matrix transform for time-series similarity measurement

ISSN号：1005-9784
期刊名称：中南工业大学学报(英文版)
时间：0
页码：802-806
语言：中文
分类：TP391.41[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术] TM711[电气工程—电力系统及自动化]
作者机构：[1]School of Physics Science and Technology, Central South University, Changsha 410083, China, [2]School of Information Science and Engineering, Central South University, Changsha 410083, China
相关基金：Projects（60634020, 60904077, 6087406% supported by the National Natural Science Foundation of China; Project（JC200903180555A） supported by the Foundation Project of Shenzhen City Science and Technology Plan of China
相关项目：面向节能降耗的有色冶金过程控制若干理论与方法研究

关键词：小波变换矩阵, 时间序列, 相似性度量, 奇异值分解方法, 特征向量, 小波方法, 序列相似性, 小波子空间, wavelet transform, singular value decomposition, inner product transform, time-series similarity

中文摘要：

一个时间系列类似测量方法基于小浪和矩阵变换被建议，并且它的反噪音能力，敏感和精确性被讨论。时间系列序列被压缩直到小浪潜水艇空间，和样品特征向量和样品时间系列序列的直角的基础被 K-L 获得变换。然后，内部产品变换被执行投射分析时间系列顺序进直角的基础获得分析特征向量。类似在样品特征向量之间被计算并且由欧几里得距离分析了特征向量。拿差错波浪例如驱动电子设备，试验性的结果证明建议方法有特征向量的低尺寸，建议方法的反噪音能力30倍大于平凡小浪的方法，建议方法的敏感是象平凡小浪方法的一样大的 1/3 ，并且建议方法的精确性比小浪的高单个价值分解方法。建议方法能在类似为 lager 时间系列数据库匹配并且索引被使用。

英文摘要：

A time-series similarity measurement method based on wavelet and matrix transform was proposed, and its anti-noise ability, sensitivity and accuracy were discussed. The time-series sequences were compressed into wavelet subspace, and sample feature vector and orthogonal basics of sample time-series sequences were obtained by K-L transform. Then the inner product transform was carried out to project analyzed time-series sequence into orthogonal basics to gain analyzed feature vectors. The similarity was calculated between sample feature vector and analyzed feature vector by the Euclid distance. Taking fault wave of power electronic devices for example, the experimental results show that the proposed method has low dimension of feature vector, the anti-noise ability of proposed method is 30 times as large as that of plain wavelet method, the sensitivity of proposed method is 1/3 as large as that of plain wavelet method, and the accuracy of proposed method is higher than that of the wavelet singular value decomposition method. The proposed method can be applied in similarity matching and indexing for lager time series databases.