Steering the LoCoMotif: Using Domain Knowledge in Time Series Motif Discovery
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910832639606784 |
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| author | Yurtman, Aras Van Wesenbeeck, Daan Meert, Wannes Blockeel, Hendrik |
| author_facet | Yurtman, Aras Van Wesenbeeck, Daan Meert, Wannes Blockeel, Hendrik |
| contents | Time Series Motif Discovery (TSMD) identifies repeating patterns in time series data, but its unsupervised nature might result in motifs that are not interesting to the user. To address this, we propose a framework that allows the user to impose constraints on the motifs to be discovered, where constraints can easily be defined according to the properties of the desired motifs in the application domain. We also propose an efficient implementation of the framework, the LoCoMotif-DoK algorithm. We demonstrate that LoCoMotif-DoK can effectively leverage domain knowledge in real and synthetic data, outperforming other TSMD techniques which only support a limited form of domain knowledge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11850 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Steering the LoCoMotif: Using Domain Knowledge in Time Series Motif Discovery Yurtman, Aras Van Wesenbeeck, Daan Meert, Wannes Blockeel, Hendrik Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Time Series Motif Discovery (TSMD) identifies repeating patterns in time series data, but its unsupervised nature might result in motifs that are not interesting to the user. To address this, we propose a framework that allows the user to impose constraints on the motifs to be discovered, where constraints can easily be defined according to the properties of the desired motifs in the application domain. We also propose an efficient implementation of the framework, the LoCoMotif-DoK algorithm. We demonstrate that LoCoMotif-DoK can effectively leverage domain knowledge in real and synthetic data, outperforming other TSMD techniques which only support a limited form of domain knowledge. |
| title | Steering the LoCoMotif: Using Domain Knowledge in Time Series Motif Discovery |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.11850 |