Revisiting WEASEL 2.0: Reproduction, Sensitivity, and an Adaptive Ensemble-Size Rule
2026-08-18 • Machine Learning
Machine Learning
AI summaryⓘ
The authors recreated WEASEL 2.0, a method that classifies time series data using special sliding windows and ensembles. They tested it on many datasets and got similar accuracy to the original results. Then, they looked closely at several design choices, finding that most were stable except the rule setting the maximum size of the ensemble. They proposed a new adaptive rule that adjusts this size based on the data, which saves memory and time without hurting accuracy, especially for long time series.
time series classificationWEASEL 2.0dilated sliding windowsensemble methodshyperparametersmemory efficiencyUCR datasetsfeature weightingadaptive rule
Authors
Cian Higgins, Gerard Carrigan, Pinar Sungu Isiacik, Georgiana Ifrim
Abstract
WEASEL 2.0 is a dictionary-based time series classifier that combines dilated sliding windows with a randomised hyperparameter ensemble and a fixed-size dense feature representation. Two of its hyperparameter choices, the maximum ensemble size and the maximum window size, are specified by simple thresholding rules whose chosen thresholds are not empirically justified in the original paper. In this work we reproduce WEASEL 2.0 on 114 UCR datasets, achieving a mean accuracy of 0.865 and median of 0.928, closely matching the published values (Wilcoxon signed-rank, p = 0.655). We then test the sensitivity of four design choices: the downstream classifier, the absence of feature weighting, the maximum window-size rule, and the maximum ensemble-size rule. The first three are robust to perturbation. The fourth is over-provisioned for long-series datasets, motivating an adaptive rule that sets the maximum ensemble size from series length and number of classes. Evaluated on fixed-length datasets, the adaptive rule reduces peak fit memory by a median of 37 MB (mean 395 MB) and fit time by a median of 0.4 s (mean 4 s), with a median accuracy change of 0% (mean -0.11%). Memory and time savings concentrate on long-series datasets where the original rule allocates the largest ensemble size.