PulseAugur
实时 13:21:42

SelF-Rocket achieves state-of-the-art accuracy in time series classification

Researchers have developed SelF-Rocket, a novel method for time series classification that builds upon the MiniRocket framework. This new approach dynamically selects optimal input representations and pooling operators during training. SelF-Rocket has demonstrated state-of-the-art performance on the University of California Riverside (UCR) benchmark datasets for time series classification. AI

影响 Introduces a novel approach to time series classification, potentially improving performance on benchmark datasets.

排序理由 This is a research paper presenting a new method for time series classification.

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

SelF-Rocket achieves state-of-the-art accuracy in time series classification

报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mouhamadou Mansour Lo, Gildas Morvan, Mathieu Rossi, Fabrice Morganti, David Mercier ·

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

    arXiv:2409.01115v5 Announce Type: replace 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 i…