Warping and Matching Subsequences Between Time Series
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916799360008192 |
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| author | Lin, Simiao Meert, Wannes Robberechts, Pieter Blockeel, Hendrik |
| author_facet | Lin, Simiao Meert, Wannes Robberechts, Pieter Blockeel, Hendrik |
| contents | Comparing time series is essential in various tasks such as clustering and classification. While elastic distance measures that allow warping provide a robust quantitative comparison, a qualitative comparison on top of them is missing. Traditional visualizations focus on point-to-point alignment and do not convey the broader structural relationships at the level of subsequences. This limitation makes it difficult to understand how and where one time series shifts, speeds up or slows down with respect to another. To address this, we propose a novel technique that simplifies the warping path to highlight, quantify and visualize key transformations (shift, compression, difference in amplitude). By offering a clearer representation of how subsequences match between time series, our method enhances interpretability in time series comparison. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15452 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Warping and Matching Subsequences Between Time Series Lin, Simiao Meert, Wannes Robberechts, Pieter Blockeel, Hendrik Machine Learning Artificial Intelligence Comparing time series is essential in various tasks such as clustering and classification. While elastic distance measures that allow warping provide a robust quantitative comparison, a qualitative comparison on top of them is missing. Traditional visualizations focus on point-to-point alignment and do not convey the broader structural relationships at the level of subsequences. This limitation makes it difficult to understand how and where one time series shifts, speeds up or slows down with respect to another. To address this, we propose a novel technique that simplifies the warping path to highlight, quantify and visualize key transformations (shift, compression, difference in amplitude). By offering a clearer representation of how subsequences match between time series, our method enhances interpretability in time series comparison. |
| title | Warping and Matching Subsequences Between Time Series |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.15452 |