Abstract In most of the current methods, the close correlation between variables and the shape characteristics of time series is neglected. In this paper, a similarity matching method for multivariate time series is proposed based on combined principal component analysis method and a shape-based improved weighted dynamic time warping algorithm(CPCA-SWDTW). Firstly, a shape coefficient is introduced and a shape based weighted dynamic time warping(SWDTW) algorithm is presented. Next, the principal components of multivariate time series are extracted as the representation, and thus the variable correlations can be eliminated. Besides, the variance devoting rate of each principal component is considered as the weight of each series. On the basis of the proposed representation, SWDTW is used to measure the similarity between transformed multivariate time series. Finally, the results of similarity search experiment show that CPCA-SWDTW is more efficient and robust. Moreover, the parameter sensitivity analysis experiment show that CPCA-SWDTW can be affected by the parameters in weight function to some extent.
Fund:Supported by of National Natural Science Foundation of China(No.71671186,71571185,71501182)
About author:: YE Yanqing, born in 1990, Ph.D. candidate. Her research interests include pattern recognition, data mining and knowledge graph.) (YANG Kewei, born in 1977, Ph.D., professor. His research interests include defense acquisition, requirement analysis and modeling and intelligent agent simulation.) (JIANG Jiang, born in 1981, Ph.D., associate professor. His research interests include evidential reasoning, uncertainty decision-making and risk ana- lysis.) (GE Bingfeng, born in 1983, Ph.D., lecturer. His research interests include conflict resolution and multi-objective programming.) (DOU Yajie, born in 1987, Ph.D., lecturer. His research interests include portfolio selection decision-making and multi-objective programming.)
YE Yanqing,YANG Kewei,JIANG Jiang等. Multivariate Time Series Similarity Matching Method Based on Weighted Dynamic Time Warping Algorithm[J]. , 2017, 30(4): 314-327.
[1] 吴绍春,吴耿锋,王 炜,等.寻找地震相关地区的时间序列相似性匹配算法.软件学报, 2006, 17(2): 185-192. (WU S C, WU G F, WANG W, et al. A Time-Sequence Similarity Matching Algorithm for Seismological Relevant Zones. Journal of Software, 2006, 17(2): 185-192.) [2] 陈立新,张 磊.一种面向金融数据的基于ARIMA的模糊时间序列预测模型.现代计算机, 2014(5): 3-8, 13. (CHEN L X, ZHANG L. A Fuzzy Time Series Forecasting Model for Financial Data Based on ARIMA. Modern Computer, 2014(5): 3-8, 13.) [3] 顾昕辰,万定生,樊 龙.基于Hadoop的水文时间序列相似性研究与应用.计算机与数字工程, 2014, 42(11): 1-6. (GU X C, WAN D S, FAN L. Research and Application of Hydrological Time Series Similarity Based on Hadoop. Computer and Di- gital Engineering, 2014, 42(11): 1-6.) [4] LEHNERTZ K. Non-linear Time Series Analysis of Intracranial EEG Recordings in Patients with Epilepsy-An Overview. International Journal of Psychophysiology, 1999, 34(1): 45-52. [5] 何晓旭.时间序列数据挖掘若干关键问题研究.博士学位论文.合肥:中国科学技术大学, 2014. (HE X X. Research on Key Issues in Time Series Data Mining. Ph.D Dissertation. Hefei, China: University of Science and Technology of China, 2014.) [6] 吴虎胜,张凤鸣,张 超,等.多元时间序列的相似性匹配.应用科 学学报, 2013, 31(6): 643-649. (WU H S, ZHANG F M, ZHANG C, et al. Matching Similar Pa- tterns for Multivariate Time Series. Journal of Applied Sciences-Electronics and Information Engineering, 2013, 31(6): 643-649.) [7] 管河山,姜青山,王声瑞.基于点分布特征的多元时间序列模式匹配方法.软件学报, 2009, 20(1): 67-79. (GUAN H S, JIANG Q S, WANG S R. Pattern Matching Method Based on Point Distribution for Multivariate Time Series. Journal of Software, 2009, 20(1): 67-79.) [8] YANG K, SHAHABI C. A PCA-Based Similarity Measure for Multivariate Time Series. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(9): 235-242. [9] YI B K, FALOUTSOS C. Fast Time Sequence Indexing for Arbitrary Lp Norms // Proc of the 26th International Conference on Very Large Data Bases. San Francesco, USA: Morgan Kaufmann Publishers, 2000: 385-394. [10] STEM H, SHMUELI M, BERMAN S. Most Discriminating Segment-Longest Common Subsequence(MDSLCS) Algorithm for Dynamic Hand Gesture Classification. Pattern Recognition Letters, 2013, 34(15): 1980-1989. [11] KEOGH E, RATANAMAHATANA C A. Exact Indexing of Dynamic Time Warping. Knowledge and Information Systems, 2005, 7(3): 358-386. [12] KIM S W, PARK S, CHU W W. An Index-Based Approach for Similarity Search Supporting Time Warping in Large Sequence Databases // Proc of the 17th International Conference on Data Engineering. Washington, USA: IEEE, 2001: 607-614. [13] GRECKI T, UCZAK M. Non-isometric Transforms in Time Series Classification Using DTW. Knowledge-Based Systems, 2014, 61: 98-108. [14] JEONG Y S, JEONG M K, OMITAOMU O A. Weighted Dynamic Time Warping for Time Series Classification. Pattern Recognition, 2011, 44(9): 2231-2240. [15] GRECKI T, UCZAK M. Multivariate Time Series Classification with Parametric Derivative Dynamic Time Warping. Expert Systems with Applications, 2015, 42(5): 2305-2312. [16] RODRIGUEZ J J, ALONSO C J, MAESTRO J A. Support Vector Machines of Interval-Based Features for Time Series Classification. Knowledge-Based Systems, 2005, 18(4/5): 171-178.
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