Time series classification with random convolution kernels based transforms: pooling operators and input representations matter

• URL: http://arxiv.org/abs/2409.01115v1
• Date: Mon, 2 Sep 2024 09:42:17 GMT
• Title: Time series classification with random convolution kernels based transforms: pooling operators and input representations matter
• Authors: Mouhamadou Mansour Lo, Gildas Morvan, Mathieu Rossi, Fabrice Morganti, David Mercier,
• Abstract summary: This article presents a new approach based on MiniRocket, called SelF-Rocket, for fast time series classification (TSC) It dynamically selects the best couple of input representations and pooling operator during the training process. It achieves state-of-the-art accuracy on the University of California Riverside (UCR) benchmark datasets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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