[1]李海林,郭韧,万校基.基于特征矩阵的多元时间序列最小距离度量方法[J].智能系统学报,2015,10(3):442-447.[doi:10.3969/j.issn.1673-4785.201405047]
LI Hailin,GUO Ren,WAN Xiaoji.A minimum distance measurement method for amultivariate time series based on the feature matrix[J].CAAI Transactions on Intelligent Systems,2015,10(3):442-447.[doi:10.3969/j.issn.1673-4785.201405047]
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LI Hailin,GUO Ren,WAN Xiaoji.A minimum distance measurement method for amultivariate time series based on the feature matrix[J].CAAI Transactions on Intelligent Systems,2015,10(3):442-447.[doi:10.3969/j.issn.1673-4785.201405047]
基于特征矩阵的多元时间序列最小距离度量方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
10
期数:
2015年第3期
页码:
442-447
栏目:
学术论文—人工智能基础
出版日期:
2015-06-25
- Title:
- A minimum distance measurement method for amultivariate time series based on the feature matrix
- Keywords:
- multivariate time series; similarity measurement; feature matrix; minimum distance; principal component analysis; Hungary algorithm; data mining
- 分类号:
- TP301
- 文献标志码:
- A
- 摘要:
- 相似性度量是多元时间序列数据挖掘任务过程中一项重要的前期工作,度量质量直接影响到后期整个数据挖掘的性能和结果.利用主成分分析方法对数据集中的每个多元时间序列数据进行特征分析,提取其特征矩阵并且构建相应的新正交坐标系.通过夹角公式来度量2个正交坐标系之间距离,并且结合匈牙利算法计算它们之间的最小距离,进而实现了一种基于特征矩阵的多元时间序列最小距离度量方法.实验结果表明,与传统方法相比,新方法具有较好的相似性度量质量,提高了多元时间序列的数据挖掘效果.
- Abstract:
- Similarity measurement is one of the most important preliminary works in the process of multivariate data mining. Its quality directly influences the performance and result of the later tasks of data mining. The data of every multivariate time series in dataset can be analyzed by the principal component analysis. The feature matrices are extracted to construct the corresponding new orthogonal coordinate systems whose distance can be measured by cosine value of the angles between two axes. Meanwhile, the Hungary algorithm is applied to the minimum distance computation of the two coordinate systems. In this way, the minimum distance measurement method for the multivariate time series based on the feature matrix is achieved. The results of experiment demonstrated that the proposed method has better quality of similarity measurement than the traditional ones and improves the effects of data mining for the multivariate time series.
- 参考文献/References:
-
[1] ESLING P, AGON C. Time-series data mining[J]. ACM Computing Surverys, 2012, 45(1): 11-12.
[2] 李海林, 杨丽彬. 时间序列数据降维及特征表示新方法[J]. 控制与决策, 2013, 28(11):1718-1722.LI Hailin, YANG Libin. Novel method of dimensionality reduction and feature representation for time series[J]. Control and Decision, 2013, 28(11):1718-1722.
[3] YANG K, SHAHABI C. An efficient k nearest neighbor search for multivariate time series[J]. Information and Computation, 2007, 205(1): 65-98.
[4] 韩敏, 李德才. 基于EOF-SVD模型的多元时间序列相关性研究及预测[J]. 系统仿真学报, 2008, 20(7): 1669-1672HAN Min, LI Decai. Multiple time series correlation extraction and prediction based on EOF-SVD model[J]. Journal of System Simulation, 2008, 20(7): 1669-1672.
[5] WENG Xiaoqing, SHEN Junyi. Classification of multivariate time series using two dimensional singular value decomposition[J]. Knowledge-Based Systems. 2008, 21(7): 535-539.
[6] 吴虎胜, 张凤鸣, 钟斌. 基于二维奇异值分解的多元时间序列相似匹配方法[J]. 电子与信息学报, 2014, 36(4): 847-854.WU Husheng, ZHANG Fengming, ZHONG Bin. Similar pattern matching method for multivariate time series based on two-dimensional singular value decomposition[J]. Journal of Electronics & Information Technology, 2014, 36(4): 847-854.
[7] 樊继聪, 王友清, 秦泗钊. 联合指标独立成分分析在多变量过程故障诊断中的应用[J]. 自动化学报, 2013, 39(5): 494-501.FAN Jicong, WANG Youqing, QIN Sizhao. Combined indices for ICA and their applications to multivariate process fault diagnosis[J]. Acta Automatica Sinica, 2013, 39(5): 494-501.
[8] 梁胜杰, 张志华, 崔立林, 等. 基于主成分分析与核独立成分分析的降维方法[J]. 系统工程与电子技术, 2011, 33(9): 2144-2148. LIANG Shengjie, ZHANG Zhihua, CUI Lilin, et al. Dimensionality reduction method based on PCA and KICA[J]. Systems Engineering and Electronics, 2011, 33(9): 2144-2148.
[9] 李正欣, 郭建胜, 惠晓滨, 等. 基于共同主成分的多元时间序列降维方法[J]. 控制与决策, 2013, 28(4): 531-536.LI Zhengxin, GUO Jiansheng, HUI Xiaobin, et al. Dimension reduction method for multivariate time series based on common principal component[J]. Control and Decision, 2013, 28(4): 531-536.
[10] 李正欣, 张凤鸣, 张晓丰, 等. 多元时间序列特征降维方法研究[J]. 小型微型计算机系统, 2013, 34(2): 338-346.LI Zhengxin, ZHANG Fengming, ZHANG Xiaofeng. Research on feature dimension reduction method for multivariate time series[J]. Journal of Chinese Computer Systems, 2013, 34(2): 338-346.
[11] LI Hailin. Asynchronism-based principal component analysis for time series data mining[J]. Expert Systems with Applications, 2014, 41(6): 2842-2850.
[12] YANKOV D, KEOGH E, REBBAPRAGADA U. Disk aware discord discovery: finding unusual time series in terabyte sized datasets[J]. Knowledge and Information Systems, 2007, 17(2): 381-390.
[13] CHEN Yanping, HU Bing, KEOGH E, et al. DTW-D: time series semi-supervised learning from a single example[C]//Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Chicago, USA, 2013: 383-391.
[14] YANG K, SHAHABI C. A PCA-based similarity measure for multivariate time series[C]//Proceedings of the 2nd ACM International Workshop on Multimedia Databases. Washington, DC, USA, 2004: 65-74.
[15] 何坚勇. 运筹学基础[M]. 北京: 清华大学出版社, 2006: 217-220.
[16] BACHE K, LICHMAN M. UCI machine learning repository. (2013-12-21)[2014-4-28]. http://archive.ics.uci.edu/ml.
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备注/Memo
收稿日期:2014-5-23;改回日期:。
基金项目:国家自然科学基金资助项目(61300139); 福建省中青年教师教育科研项目(JAS14024); 华侨大学中青年教师科研提升资助计划项目(ZQN-PY220).
作者简介:李海林,男,1982年生,副教授,博士,主要研究方向为数据挖掘与决策支持, 主持国家自然科学基金和省部级青年基金各1项,发表学术论文30余篇,其中被SCI检索7篇、EI检索10余篇.郭韧,女,1975年生,讲师,博士研究生,主要研究方向为知识管理与数据挖掘,发表学术论文近20篇,其中被CSSCI检索9篇.万校基,男,1982年生,讲师,博士,主要研究方向为数据挖掘与决策支持,发表学术论文10余篇.
通讯作者:李海林. E-mail: hailin@mail.dlut.edu.cn.
更新日期/Last Update:
2015-07-15