Multivariate Time Series Similarity Searching

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 similar...

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Main Authors: Jimin Wang, Yuelong Zhu, Shijin Li, Dingsheng Wan, Pengcheng Zhang
Format: Article
Language:English
Published: Wiley 2014-01-01
Series:The Scientific World Journal
Online Access:http://dx.doi.org/10.1155/2014/851017
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author Jimin Wang
Yuelong Zhu
Shijin Li
Dingsheng Wan
Pengcheng Zhang
author_facet Jimin Wang
Yuelong Zhu
Shijin Li
Dingsheng Wan
Pengcheng Zhang
author_sort Jimin Wang
collection DOAJ
description 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 SPCA, 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.
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institution Kabale University
issn 2356-6140
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language English
publishDate 2014-01-01
publisher Wiley
record_format Article
series The Scientific World Journal
spelling doaj-art-588e31460e72430285381b7aebdb2b272025-02-03T01:32:46ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/851017851017Multivariate Time Series Similarity SearchingJimin Wang0Yuelong Zhu1Shijin Li2Dingsheng Wan3Pengcheng Zhang4College of Computer & Information, Hohai University, Nanjing 210098, ChinaCollege of Computer & Information, Hohai University, Nanjing 210098, ChinaCollege of Computer & Information, Hohai University, Nanjing 210098, ChinaCollege of Computer & Information, Hohai University, Nanjing 210098, ChinaCollege of Computer & Information, Hohai University, Nanjing 210098, ChinaMultivariate 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 SPCA, 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.http://dx.doi.org/10.1155/2014/851017
spellingShingle Jimin Wang
Yuelong Zhu
Shijin Li
Dingsheng Wan
Pengcheng Zhang
Multivariate Time Series Similarity Searching
The Scientific World Journal
title Multivariate Time Series Similarity Searching
title_full Multivariate Time Series Similarity Searching
title_fullStr Multivariate Time Series Similarity Searching
title_full_unstemmed Multivariate Time Series Similarity Searching
title_short Multivariate Time Series Similarity Searching
title_sort multivariate time series similarity searching
url http://dx.doi.org/10.1155/2014/851017
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AT yuelongzhu multivariatetimeseriessimilaritysearching
AT shijinli multivariatetimeseriessimilaritysearching
AT dingshengwan multivariatetimeseriessimilaritysearching
AT pengchengzhang multivariatetimeseriessimilaritysearching