Time series classification with random convolution kernels: pooling operators and input representations matter
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914514688016384 |
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| author | Lo, Mouhamadou Mansour Morvan, Gildas Rossi, Mathieu Morganti, Fabrice Mercier, David |
| author_facet | Lo, Mouhamadou Mansour Morvan, Gildas Rossi, Mathieu Morganti, Fabrice Mercier, David |
| contents | This article presents a new approach based on MiniRocket, called SelF-Rocket, for fast time series classification (TSC). Unlike existing approaches based on random convolution kernels, it dynamically selects the best couple of input representations and pooling operator during the training process. SelF-Rocket achieves state-of-the-art accuracy on the University of California Riverside (UCR) TSC benchmark datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_01115 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Time series classification with random convolution kernels: pooling operators and input representations matter Lo, Mouhamadou Mansour Morvan, Gildas Rossi, Mathieu Morganti, Fabrice Mercier, David Machine Learning This article presents a new approach based on MiniRocket, called SelF-Rocket, for fast time series classification (TSC). Unlike existing approaches based on random convolution kernels, it dynamically selects the best couple of input representations and pooling operator during the training process. SelF-Rocket achieves state-of-the-art accuracy on the University of California Riverside (UCR) TSC benchmark datasets. |
| title | Time series classification with random convolution kernels: pooling operators and input representations matter |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.01115 |