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

Mouhamadou Mansour Lo; Gildas Morvan; Mathieu Rossi; Fabrice Morganti; David Mercier

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arXiv · 2409.01115

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

Mouhamadou Mansour Lo·Gildas Morvan·Mathieu Rossi·Fabrice Morganti·David Mercier

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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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Mouhamadou Mansour Lo, Gildas Morvan, Mathieu Rossi, Fabrice Morganti, David Mercier. 2024-09-02. Time series classification with random convolution kernels: pooling operators and input representations matter. https://arxiv.org/abs/2409.01115

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