A New Time Series Similarity Measurement Method Based on the Morphological Pattern and Symbolic Aggregate Approximation
Aiming at the problem that the traditional similarity measurement methods cannot effectively measure the similarity of the time series with the difference both in the trend and detail, this paper proposes a new time series similarity measurement method (MP-SAX) based on the morphological pattern (MP...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8793127/ |
| _version_ | 1818611583328190464 |
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| author | Jiancheng Yin Rixin Wang Huailiang Zheng Yuantao Yang Yuqing Li Minqiang Xu |
| author_facet | Jiancheng Yin Rixin Wang Huailiang Zheng Yuantao Yang Yuqing Li Minqiang Xu |
| author_sort | Jiancheng Yin |
| collection | DOAJ |
| description | Aiming at the problem that the traditional similarity measurement methods cannot effectively measure the similarity of the time series with the difference both in the trend and detail, this paper proposes a new time series similarity measurement method (MP-SAX) based on the morphological pattern (MP) and symbolic aggregate approximation (SAX). According to the empirical mode decomposition (EMD), the time series are decomposed and reconstructed into the trend component and the detail component. Then, the similarity of the trend component under morphological pattern coding and that of the detail component under symbolic aggregate approximation coding are respectively calculated by the longest common subsequence (LCS). Finally, the similarity of the time series is obtained by weighted aggregation of the similarity of trend component and detail component. The MP-SAX is verified by the simulation time series and the time series from UCR Time Series Classification/Clustering Homepage. The results show that the MP-SAX can effectively measure the similarity of the time series with the changes both in trend and detail. |
| first_indexed | 2024-12-16T15:32:38Z |
| format | Article |
| id | doaj.art-06dfafb9e64a4f04bac6a52861d50ed3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T15:32:38Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-06dfafb9e64a4f04bac6a52861d50ed32022-12-21T22:26:17ZengIEEEIEEE Access2169-35362019-01-01710975110976210.1109/ACCESS.2019.29341098793127A New Time Series Similarity Measurement Method Based on the Morphological Pattern and Symbolic Aggregate ApproximationJiancheng Yin0https://orcid.org/0000-0003-0844-4418Rixin Wang1Huailiang Zheng2https://orcid.org/0000-0003-0391-4679Yuantao Yang3Yuqing Li4Minqiang Xu5Deep Space Exploration Research Center, Harbin Institute of Technology, Harbin, ChinaDeep Space Exploration Research Center, Harbin Institute of Technology, Harbin, ChinaDeep Space Exploration Research Center, Harbin Institute of Technology, Harbin, ChinaDeep Space Exploration Research Center, Harbin Institute of Technology, Harbin, ChinaDeep Space Exploration Research Center, Harbin Institute of Technology, Harbin, ChinaDeep Space Exploration Research Center, Harbin Institute of Technology, Harbin, ChinaAiming at the problem that the traditional similarity measurement methods cannot effectively measure the similarity of the time series with the difference both in the trend and detail, this paper proposes a new time series similarity measurement method (MP-SAX) based on the morphological pattern (MP) and symbolic aggregate approximation (SAX). According to the empirical mode decomposition (EMD), the time series are decomposed and reconstructed into the trend component and the detail component. Then, the similarity of the trend component under morphological pattern coding and that of the detail component under symbolic aggregate approximation coding are respectively calculated by the longest common subsequence (LCS). Finally, the similarity of the time series is obtained by weighted aggregation of the similarity of trend component and detail component. The MP-SAX is verified by the simulation time series and the time series from UCR Time Series Classification/Clustering Homepage. The results show that the MP-SAX can effectively measure the similarity of the time series with the changes both in trend and detail.https://ieeexplore.ieee.org/document/8793127/Similarity measuremorphological patternsymbolic aggregate approximationlongest common subsequenceempirical mode decomposition |
| spellingShingle | Jiancheng Yin Rixin Wang Huailiang Zheng Yuantao Yang Yuqing Li Minqiang Xu A New Time Series Similarity Measurement Method Based on the Morphological Pattern and Symbolic Aggregate Approximation IEEE Access Similarity measure morphological pattern symbolic aggregate approximation longest common subsequence empirical mode decomposition |
| title | A New Time Series Similarity Measurement Method Based on the Morphological Pattern and Symbolic Aggregate Approximation |
| title_full | A New Time Series Similarity Measurement Method Based on the Morphological Pattern and Symbolic Aggregate Approximation |
| title_fullStr | A New Time Series Similarity Measurement Method Based on the Morphological Pattern and Symbolic Aggregate Approximation |
| title_full_unstemmed | A New Time Series Similarity Measurement Method Based on the Morphological Pattern and Symbolic Aggregate Approximation |
| title_short | A New Time Series Similarity Measurement Method Based on the Morphological Pattern and Symbolic Aggregate Approximation |
| title_sort | new time series similarity measurement method based on the morphological pattern and symbolic aggregate approximation |
| topic | Similarity measure morphological pattern symbolic aggregate approximation longest common subsequence empirical mode decomposition |
| url | https://ieeexplore.ieee.org/document/8793127/ |
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