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The Scientific World Journal
Volume 2014, Article ID 851017, 8 pages
Research Article

Multivariate Time Series Similarity Searching

College of Computer & Information, Hohai University, Nanjing 210098, China

Received 21 February 2014; Revised 4 April 2014; Accepted 13 April 2014; Published 8 May 2014

Academic Editor: Jaesoo Yoo

Copyright © 2014 Jimin Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [5 citations]

The following is the list of published articles that have cited the current article.

  • Aminata Kane, “Trend and Value Based Time Series Representation for Similarity Search,” 2017 IEEE Third International Conference on Multimedia Big Data (BigMM), pp. 252–259, . View at Publisher · View at Google Scholar
  • Vladimir Bure, and Kseniya Staroverova, “Methods of cluster analysis for detection of homogeneous groups of healthcare time series,” 2017 Constructive Nonsmooth Analysis and Related Topics (dedicated to the memory of V.F. Demyanov) (CNSA), pp. 1–4, . View at Publisher · View at Google Scholar
  • Liying Fang, Han Zhao, Pu Wang, Mingwei Yu, Jianzhuo Yan, Wenshuai Cheng, and Peiyu Chen, “Feature selection method based on mutual information and class separability for dimension reduction in multidimensional time series for clinical data,” Biomedical Signal Processing and Control, vol. 21, pp. 82–89, 2015. View at Publisher · View at Google Scholar
  • Zheng-Xin Li, Chao Li, Feng-Ming Zhang, Xiao-Feng Zhang, and Ji-Cheng Chen, “Survey of similarity search for multivariate time series,” Kongzhi yu Juece/Control and Decision, vol. 32, no. 4, pp. 577–583, 2017. View at Publisher · View at Google Scholar
  • Nidhi Garg, Amandeep Bisht, Hardeep S. Ryait, and Amod Kumar, “Identification of motion outliers in wrist pulse signal,” Computers & Electrical Engineering, 2018. View at Publisher · View at Google Scholar