The ROCKET family has since grown rapidly. MiniRocket stripped the original method down to a nearly deterministic transform with a fixed set of 84 kernel patterns containing only the values -1 and 2, dramatically accelerating convolution while keeping accuracy essentially unchanged. MultiRocket enriched the recipe by adding new pooling operators and a first-order difference representation of the input. HYDRA blended random convolutions with dictionary-based ideas, grouping kernels and building histograms of their strongest responses. Across benchmarks, these methods consistently sit at or near the state of the art, and because the only learned parameters are the weights of a linear classifier, they remain among the fastest options available. Yet a closer look reveals a hidden rigidity: nearly all of them commit, at design time, to a fixed input representation and a small, fixed menu of pooling operators.

A new study published in Data Mining and Knowledge Discovery argues that this rigidity is a genuine weakness. Mouhamadou Mansour Lo and colleagues at the University of Artois in France first ran a systematic experiment on the UCR time series archive, the standard benchmark collection for the field. They took MiniRocket and systematically swapped its pooling operator, testing five candidates: the proportion of positive values (PPV), zero crossing (ZC), the mean of positive values, the mean of the indices of positive values, and the longest stretch of positive values. They also varied the input representation, comparing the raw series, its first-order difference, and a concatenation of both. Across 112 datasets and 30 resamples each, the result was striking: no single combination of representation and pooling operator dominated. Even the best average performer, PPV applied to the mixed representation, was beaten on roughly 83 percent of individual datasets by some alternative.

The study did not stop at the aggregate numbers. The authors probed when each pooling operator shines, using synthetic activation maps in which two classes differ in a controlled way. When classes differ mainly in the amplitude of a discriminative pattern, the mean of positive values separates them best. When the pattern’s typical location shifts between classes, the mean of indices of positive values wins. When the pattern’s duration varies by class, the longest stretch of positive values is the strongest discriminator. PPV and zero crossing act as versatile generalists, coping well when a pattern simply appears at different frequencies in each class. A further regularity emerged with series length: PPV tends to excel on short time series, while zero crossing pulls ahead on longer ones, plausibly because longer sequences accumulate richer information in their sign changes. This observation also helps explain why MiniRocket, which relies entirely on PPV, performs so well on the UCR archive, which is dominated by short series.

Motivated by these findings, the team built SelF-Rocket, short for Selected Features Rocket, an extension of MiniRocket that refuses to commit in advance. Instead of fixing the pooling operator and input representation, SelF-Rocket generates features under all fifteen combinations of three input representations and five pooling operators, and then lets the data decide. The heart of the method is a wrapper-based feature selection module. The training set is repeatedly split into stratified train and validation subsets, either through repeated stratified k-fold cross-validation or a stratified shuffle split, depending on dataset size. For each representation-pooling combination, a set of small ridge classifiers, the same linear model used by the original ROCKET, is trained on these splits, and the combination with the highest median validation accuracy is chosen. The median, rather than the mean, guards against lucky runs on unusually easy splits.

One subtlety proved important for small datasets: vote validation. When a dataset contains few examples, the mini-classifiers inside the selection module are trained on very little data, and their votes can be unreliable. SelF-Rocket therefore checks whether the winning combination is broadly supported, requiring it to appear near the top of most voters’ rankings within a configurable threshold. If the consensus is too weak, the method falls back to a sensible default, PPV or zero crossing on the mixed representation, chosen according to the length of the series. Across 112 datasets and 30 resamples, the fallback fired in about 41 percent of runs, and its benefit was most pronounced on datasets with at most 100 training examples, where an ill-validated vote could otherwise steer the classifier badly wrong.

The benchmark results are impressive. Evaluated on the same 112 UCR datasets and the same 30 train-test resamples used in recent community-wide bake-offs, SelF-Rocket improved on its baseline MiniRocket by roughly one percentage point of mean accuracy. Combined with HYDRA’s features, it ranked second only to HIVE-COTE 2.0 among eleven compared classifiers, edging out MultiRocket and Hydra plus MultiRocket on average, and the Wilcoxon signed-rank test found no statistically significant difference between Hydra plus SelF-Rocket, Hydra plus MultiRocket, and HIVE-COTE 2.0. In other words, a method that uses only a single pooling operator at a time matches the accuracy of methods that stack four operators, while enjoying a practical advantage at prediction time: SelF-Rocket feeds its classifier only 9,996 or 19,992 features, depending on the representation, compared with roughly 50,000 for MultiRocket, which translates into faster classification.

The authors are candid about the costs and the remaining headroom. Feature generation is heavier than MiniRocket’s, since roughly 9,996 kernels are instantiated per representation type, and the selection module adds training time, though with the mixed representation and default settings the module takes under two seconds per resample on average. Ablation studies showed that removing any single pooling operator does not significantly change accuracy, but removing PPV hurts more than removing the longest-stretch operator, and the mixed representation generally outperforms either raw or differenced inputs alone. To quantify the ceiling, the team also ran an oracle version of SelF-Rocket, in which the best representation-pooling pair is known in advance for each dataset. The oracle’s results sketch how much performance a better selection module might one day unlock, a question the authors leave explicitly open.

The implications reach beyond one benchmark. Random convolution kernel methods are already prized in domains from healthcare to industrial monitoring precisely because they are fast, simple, and accurate; making them adaptive removes a design decision that practitioners would otherwise have to guess. The implementation is open source on GitHub, and the authors outline clear next steps: richer input representations such as second-order differences, Fourier or Hilbert transforms, faster filter-based selection using chi-squared tests, and improved vote validation to close the gap to the oracle. For a field that has spent years squeezing accuracy out of fixed recipes, the message of this study is refreshingly simple: the best features depend on the data, and a classifier that checks, rather than assumes, is measurably better for it.

Subject of Research: Adaptive feature selection with random convolution kernels for time series classification

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

Article References: Lo, M. M., Morvan, G., Rossi, M., Morganti, F., & Mercier, D. (2026). Time series classification with random convolution kernels: pooling operators and input representations matter. Data Mining and Knowledge Discovery, 40(6), Article 100. https://doi.org/10.1007/s10618-026-01269-w

Image Credits: AI Generated

DOI: 10.1007/s10618-026-01269-w

Keywords: time series classification, random convolution kernels, ROCKET, MiniRocket, pooling operators, feature selection, machine learning, SelF-Rocket, UCR archive, ridge classifier, HYDRA, MultiRocket