AdaPDTW: An Efficient Abstract-Adaptive Piecewise Dynamic Time Warping for Time Series Classification
Dynamic Time Warping (DTW) offers precise similarity measure but suffers from high computational cost. To address this issue, we propose an abstract-adaptive PAR-DTW, which computes DTW in a low-dimensional piecewise abstract representation (PAR) space. Unlike existing methods that use globally unif...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10994439/ |
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| author | Qinglin Cai Leiying Chen Jian Shao Ling Chen |
| author_facet | Qinglin Cai Leiying Chen Jian Shao Ling Chen |
| author_sort | Qinglin Cai |
| collection | DOAJ |
| description | Dynamic Time Warping (DTW) offers precise similarity measure but suffers from high computational cost. To address this issue, we propose an abstract-adaptive PAR-DTW, which computes DTW in a low-dimensional piecewise abstract representation (PAR) space. Unlike existing methods that use globally uniform pointwise measures, our approach clusters abstracts and learns adaptive pointwise measures for each cluster pair. We formulate a joint objective function that combines global classification loss with local clustering loss, and introduce an efficient optimization method based on closed-form solutions. This enhances local adaptability while significantly reducing computational complexity. Experimental results demonstrate that our method achieves precision comparable to DTW and significantly outperforms existing PAR-DTW methods, offering up to four orders of magnitude in speedup. |
| format | Article |
| id | doaj-art-d1b0269e536c43549d5dc34c79f1b8d0 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d1b0269e536c43549d5dc34c79f1b8d02025-08-20T02:25:47ZengIEEEIEEE Access2169-35362025-01-0113841888420110.1109/ACCESS.2025.356845310994439AdaPDTW: An Efficient Abstract-Adaptive Piecewise Dynamic Time Warping for Time Series ClassificationQinglin Cai0https://orcid.org/0000-0001-5219-7771Leiying Chen1https://orcid.org/0009-0009-3673-1423Jian Shao2Ling Chen3https://orcid.org/0000-0003-1934-5992Donghai Laboratory, Zhoushan, ChinaApple Inc., Shanghai, ChinaCollege of Computer Science and Technology, Zhejiang University, Hangzhou, ChinaCollege of Computer Science and Technology, Zhejiang University, Hangzhou, ChinaDynamic Time Warping (DTW) offers precise similarity measure but suffers from high computational cost. To address this issue, we propose an abstract-adaptive PAR-DTW, which computes DTW in a low-dimensional piecewise abstract representation (PAR) space. Unlike existing methods that use globally uniform pointwise measures, our approach clusters abstracts and learns adaptive pointwise measures for each cluster pair. We formulate a joint objective function that combines global classification loss with local clustering loss, and introduce an efficient optimization method based on closed-form solutions. This enhances local adaptability while significantly reducing computational complexity. Experimental results demonstrate that our method achieves precision comparable to DTW and significantly outperforms existing PAR-DTW methods, offering up to four orders of magnitude in speedup.https://ieeexplore.ieee.org/document/10994439/Time seriesdistance metric learningdynamic time warpingclassification |
| spellingShingle | Qinglin Cai Leiying Chen Jian Shao Ling Chen AdaPDTW: An Efficient Abstract-Adaptive Piecewise Dynamic Time Warping for Time Series Classification IEEE Access Time series distance metric learning dynamic time warping classification |
| title | AdaPDTW: An Efficient Abstract-Adaptive Piecewise Dynamic Time Warping for Time Series Classification |
| title_full | AdaPDTW: An Efficient Abstract-Adaptive Piecewise Dynamic Time Warping for Time Series Classification |
| title_fullStr | AdaPDTW: An Efficient Abstract-Adaptive Piecewise Dynamic Time Warping for Time Series Classification |
| title_full_unstemmed | AdaPDTW: An Efficient Abstract-Adaptive Piecewise Dynamic Time Warping for Time Series Classification |
| title_short | AdaPDTW: An Efficient Abstract-Adaptive Piecewise Dynamic Time Warping for Time Series Classification |
| title_sort | adapdtw an efficient abstract adaptive piecewise dynamic time warping for time series classification |
| topic | Time series distance metric learning dynamic time warping classification |
| url | https://ieeexplore.ieee.org/document/10994439/ |
| work_keys_str_mv | AT qinglincai adapdtwanefficientabstractadaptivepiecewisedynamictimewarpingfortimeseriesclassification AT leiyingchen adapdtwanefficientabstractadaptivepiecewisedynamictimewarpingfortimeseriesclassification AT jianshao adapdtwanefficientabstractadaptivepiecewisedynamictimewarpingfortimeseriesclassification AT lingchen adapdtwanefficientabstractadaptivepiecewisedynamictimewarpingfortimeseriesclassification |