mlsanityData to 7 Oct 2026

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

arXiv 2409.01115 ↗First posted 2 Sep 2024cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 29 references that are in the map. First posted on arXiv 2 Sep 2024.

Builds on

15 of 29 references.

References · 15

  1. 2018The UCR Time Series Archivecited by 421
  2. 2019InceptionTime: Finding AlexNet for Time Series Classificationcited by 386
  3. 2019ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernelscited by 336
  4. 2020MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classificationcited by 186
  5. 2021HIVE-COTE 2.0: a new meta ensemble for time series classificationcited by 118
  6. 2023Bake off redux: a review and experimental evaluation of recent time series classification algorithmscited by 99
  7. 2021MultiRocket: Multiple pooling operators and transformations for fast and effective time series classificationcited by 89
  8. 2022HYDRA: Competing convolutional kernels for fast and accurate time series classificationcited by 53
  9. 2024aeon: a Python toolkit for learning from time seriescited by 46
  10. 2023An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Setcited by 27
  11. 2023QUANT: A Minimalist Interval Method for Time Series Classificationcited by 15
  12. 2023Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernelscited by 7
3 more
  1. 2022S-Rocket: Selective Random Convolution Kernels for Time Series Classificationcited by 6
  2. 2023Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time seriescited by 5
  3. 2023POCKET: Pruning Random Convolution Kernels for Time Series Classification from a Feature Selection Perspectivecited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Negation Beyond the Verbal Channel: Temporal Multimodal Correlates in Dialoguecited by 1
  2. 2026Multi-Class Electrical and Mechanical Fault Classification Using Random Convolutional Kernelscited by 0