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

arXiv:2409.01115 · cs.LG · Submitted 2024-09-02 · Read on arXiv

cs.LG

Submitted: 2024-09-02

Updated: 2026-04-29

Comments: v1: initial version, incorrect evaluation. v2: Method improved, evaluation corrected, title simplified. v3: Add acknowledgments. v4: text correction

Journal ref: Data Min Knowl Disc 40, 100 (2026)

DOI: 10.1007/s10618-026-01269-w

Code: https://github.com/ANR-MYEL/SelF-Rocket

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 70× in this paper · explained in 1 episode
• Feature Selection — 33× in this paper · explained in 4 episodes
• Time Series Classification — 19× in this paper
• Benchmark — 6× in this paper · explained in 2 episodes
• Multiple Comparison Matrix — 4× in this paper
• University of California Riverside — 3× in this paper
• Proportion of Positive Values — 2× in this paper
• Pruning — 2× in this paper · explained in 4 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Dynamic Time Warping — 1× in this paper
• Elastic Ensemble — 1× in this paper
• Global Maximum Pooling — 1× in this paper
• Initial Data — 1× in this paper · explained in 2 episodes
• Mean of Indices of Positive Values — 1× in this paper
• Mean of Positive Values — 1× in this paper
• Zero Crossing — 1× in this paper

Sources

• S-Rocket: Selective Random Convolution Kernels for Time Series Classification
• The UCR Time Series Archive
• An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set

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