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arxiv: 2409.01115 · v5 · submitted 2024-09-02 · 💻 cs.LG

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

Mouhamadou Mansour Lo , Gildas Morvan , Mathieu Rossi , Fabrice Morganti , David Mercier This is my paper

Pith reviewed 2026-05-23 21:21 UTC · model grok-4.3

classification 💻 cs.LG
keywords time series classificationrandom convolution kernelsMiniRocketSelF-Rocketpooling operatorsinput representationsUCR benchmark
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The pith

SelF-Rocket dynamically selects the best input representations and pooling operators during training to achieve state-of-the-art accuracy on time series classification benchmarks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents SelF-Rocket as a new method for fast time series classification that builds on MiniRocket by dynamically choosing the best pair of input representation and pooling operator. This is done to exploit the fact that these choices matter for performance. A reader would care if the selection leads to better accuracy while keeping the computational efficiency of random kernel approaches. The work reports that this results in top performance on the UCR collection of datasets.

Core claim

SelF-Rocket, based on MiniRocket, dynamically selects the best couple of input representations and pooling operator during the training process, achieving state-of-the-art accuracy on the University of California Riverside TSC benchmark datasets.

What carries the argument

Dynamic selection of the optimal input representation and pooling operator pair during training in a random convolution kernel based classifier.

Load-bearing premise

The dynamic selection process does not introduce overfitting or selection bias that inflates the reported accuracy on the benchmark datasets.

What would settle it

Running SelF-Rocket on a separate set of time series classification datasets and comparing its accuracy to MiniRocket and other methods would test if the gains hold.

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces SelF-Rocket, an extension of MiniRocket for time series classification that dynamically selects the optimal pair of input representation and pooling operator during training. It reports state-of-the-art accuracy on the UCR TSC benchmark datasets.

Significance. If the selection procedure is shown to be free of dataset-specific overfitting, the method could provide a practical way to adapt random convolution kernels to varying time series properties, extending the utility of MiniRocket-style approaches beyond fixed configurations.

major comments (2)
  1. [§4] §4 (Experimental Setup): The description of the dynamic selection process does not specify whether the choice of representation/pooling pair is performed via nested cross-validation (with selection hyperparameters frozen before test-set evaluation) or via per-dataset tuning on validation splits; without this, the SOTA claim on the fixed UCR collection risks capitalizing on dataset quirks rather than demonstrating generalization.
  2. [Table 2] Table 2 (Accuracy Results): The reported accuracies for SelF-Rocket are presented without error bars, number of independent runs, or statistical significance tests against MiniRocket and other baselines; this undermines the reliability of the headline performance claim.
minor comments (2)
  1. [Abstract] Abstract: The claim of SOTA accuracy is stated without reference to the experimental protocol or baselines, which would strengthen the summary.
  2. [§3.2] §3.2: The notation distinguishing the candidate input representations could be accompanied by a short illustrative equation for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight important aspects of experimental rigor. We address each major comment below and will incorporate clarifications and additional analyses in the revised manuscript.

read point-by-point responses

  1. Referee: [§4] §4 (Experimental Setup): The description of the dynamic selection process does not specify whether the choice of representation/pooling pair is performed via nested cross-validation (with selection hyperparameters frozen before test-set evaluation) or via per-dataset tuning on validation splits; without this, the SOTA claim on the fixed UCR collection risks capitalizing on dataset quirks rather than demonstrating generalization.

    Authors: We agree that §4 does not provide sufficient detail on the selection procedure. The current implementation selects the representation/pooling pair on a per-dataset basis by evaluating candidates on a validation split drawn from the training data (typically 20% hold-out), without nested cross-validation. This matches standard practice for UCR benchmark comparisons but does introduce the risk of dataset-specific adaptation noted by the referee. We will revise §4 to explicitly describe this process, add a limitations paragraph discussing potential overfitting to UCR dataset characteristics, and include results for a fixed (non-per-dataset) selection strategy to strengthen the generalization claim. revision: yes

  2. Referee: [Table 2] Table 2 (Accuracy Results): The reported accuracies for SelF-Rocket are presented without error bars, number of independent runs, or statistical significance tests against MiniRocket and other baselines; this undermines the reliability of the headline performance claim.

    Authors: We acknowledge that Table 2 reports point estimates only. Although the core kernel generation is efficient, the random components mean results can vary with seed. We will rerun all experiments across 10 independent random seeds, report mean accuracy with standard deviation in the revised Table 2, and add pairwise statistical significance tests (Wilcoxon signed-rank with Holm correction) against MiniRocket and the other baselines to support the performance claims. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical method presentation

full rationale

The paper describes SelF-Rocket as an empirical extension of MiniRocket that dynamically selects input representation and pooling operator pairs during training, reporting SOTA accuracy on the UCR TSC benchmark. No derivation chain, equations, or first-principles results are referenced in the abstract or method summary. The central claim is benchmark performance from a selection procedure, not a mathematical reduction where a 'prediction' equals a fitted input by construction, nor any self-definitional loop, uniqueness theorem imported from self-citation, or ansatz smuggled via prior work. The selection mechanism is a training heuristic whose validity rests on external benchmark evaluation rather than internal equivalence to inputs. This is the common case of a self-contained empirical contribution with no detectable circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no equations, so no free parameters, axioms, or invented entities can be extracted.

pith-pipeline@v0.9.0 · 5594 in / 936 out tokens · 17051 ms · 2026-05-23T21:21:39.981689+00:00 · methodology

discussion (0)

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Reference graph

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