Similarity join over multiple time series under Dynamic Time Warping
Similarity join over multiple time series is an interesting task of data mining. This task aims at identifying couples of similar subsequences from multiple time series and the two subsequences might have any length and be at any position in the time series. However, the task is extremely challengi...
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Can Tho University Publisher
2023-10-01
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| Series: | CTU Journal of Innovation and Sustainable Development |
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| Online Access: | http://web2010.thanhtoan/index.php/ctujs/article/view/671 |
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| author | Bui Cong Giao |
| author_facet | Bui Cong Giao |
| author_sort | Bui Cong Giao |
| collection | DOAJ |
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Similarity join over multiple time series is an interesting task of data mining. This task aims at identifying couples of similar subsequences from multiple time series and the two subsequences might have any length and be at any position in the time series. However, the task is extremely challenging since the computational time to search for couples of similar subsequences from two time series is very large. Moreover, the task needs to normalize two subsequences before conducting a distance measure on the normalized subsequences to consider the similar degree of the original subsequences. To address the problem, this paper proposes a method of similarity join over two time series under Dynamic Time Warping (DTW), supporting z-score normalization. The proposed method utilizes both a suite of state-of-the-art techniques for computing the DTW distance and a technique of incremental z-score normalization to reduce the computational costs. The method employs multithreading to improve runtime performance. If similar subsequences from two time series may not pair up because they are too far apart, the method might use a sliding window to constrain a scope for coupling similar subsequences. The experiments on the proposed method show that the method could return similar subsequences quickly and incur no false dismissals.
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| format | Article |
| id | doaj-art-b37b067631124c318815c77aecb6298c |
| institution | OA Journals |
| issn | 2588-1418 2815-6412 |
| language | English |
| publishDate | 2023-10-01 |
| publisher | Can Tho University Publisher |
| record_format | Article |
| series | CTU Journal of Innovation and Sustainable Development |
| spelling | doaj-art-b37b067631124c318815c77aecb6298c2025-08-20T02:02:10ZengCan Tho University PublisherCTU Journal of Innovation and Sustainable Development2588-14182815-64122023-10-0115Special issue: ISDSSimilarity join over multiple time series under Dynamic Time WarpingBui Cong Giao0a:1:{s:5:"en_US";s:3:"SGU";} Similarity join over multiple time series is an interesting task of data mining. This task aims at identifying couples of similar subsequences from multiple time series and the two subsequences might have any length and be at any position in the time series. However, the task is extremely challenging since the computational time to search for couples of similar subsequences from two time series is very large. Moreover, the task needs to normalize two subsequences before conducting a distance measure on the normalized subsequences to consider the similar degree of the original subsequences. To address the problem, this paper proposes a method of similarity join over two time series under Dynamic Time Warping (DTW), supporting z-score normalization. The proposed method utilizes both a suite of state-of-the-art techniques for computing the DTW distance and a technique of incremental z-score normalization to reduce the computational costs. The method employs multithreading to improve runtime performance. If similar subsequences from two time series may not pair up because they are too far apart, the method might use a sliding window to constrain a scope for coupling similar subsequences. The experiments on the proposed method show that the method could return similar subsequences quickly and incur no false dismissals. http://web2010.thanhtoan/index.php/ctujs/article/view/671Data normalizationDynamic Time Warpingsimilarity join |
| spellingShingle | Bui Cong Giao Similarity join over multiple time series under Dynamic Time Warping CTU Journal of Innovation and Sustainable Development Data normalization Dynamic Time Warping similarity join |
| title | Similarity join over multiple time series under Dynamic Time Warping |
| title_full | Similarity join over multiple time series under Dynamic Time Warping |
| title_fullStr | Similarity join over multiple time series under Dynamic Time Warping |
| title_full_unstemmed | Similarity join over multiple time series under Dynamic Time Warping |
| title_short | Similarity join over multiple time series under Dynamic Time Warping |
| title_sort | similarity join over multiple time series under dynamic time warping |
| topic | Data normalization Dynamic Time Warping similarity join |
| url | http://web2010.thanhtoan/index.php/ctujs/article/view/671 |
| work_keys_str_mv | AT buiconggiao similarityjoinovermultipletimeseriesunderdynamictimewarping |