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Multivariate Time Series Similarity Searching
Joint Authors
Zhu, Yuelong
Li, Shijin
Wan, Dingsheng
Wang, Jimin
Zhang, Pengcheng
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-05-08
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Multivariate time series (MTS) datasets are very common in various financial, multimedia, and hydrological fields.
In this paper, a dimension-combination method is proposed to search similar sequences for MTS.
Firstly, the similarity of single-dimension series is calculated; then the overall similarity of the MTS is obtained by synthesizing each of the single-dimension similarity based on weighted BORDA voting method.
The dimension-combination method could use the existing similarity searching method.
Several experiments, which used the classification accuracy as a measure, were performed on six datasets from the UCI KDD Archive to validate the method.
The results show the advantage of the approach compared to the traditional similarity measures, such as Euclidean distance (ED), cynamic time warping (DTW), point distribution (PD), PCA similarity factor S PCA , and extended Frobenius norm (Eros), for MTS datasets in some ways.
Our experiments also demonstrate that no measure can fit all datasets, and the proposed measure is a choice for similarity searches.
American Psychological Association (APA)
Wang, Jimin& Zhu, Yuelong& Li, Shijin& Wan, Dingsheng& Zhang, Pengcheng. 2014. Multivariate Time Series Similarity Searching. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1051337
Modern Language Association (MLA)
Wang, Jimin…[et al.]. Multivariate Time Series Similarity Searching. The Scientific World Journal No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-1051337
American Medical Association (AMA)
Wang, Jimin& Zhu, Yuelong& Li, Shijin& Wan, Dingsheng& Zhang, Pengcheng. Multivariate Time Series Similarity Searching. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1051337
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1051337