Pruning Extensions and Efficiency Trade-Offs for Sustainable Time Series Classification
Time series classification (TSC) enables important use cases, however lacks a unified understanding of performance trade-offs across models, datasets, and hardware. While resource awareness has grown in the field, TSC methods have not yet been rigorously evaluated for energy efficiency. This paper introduces a holistic evaluation framework that explicitly explores the balance of predictive performance and resource consumption in TSC. To boost efficiency, we apply a theoretically bounded pruning strategy to leading hybrid classifiers - Hydra and Quant - and present Hydrant, a novel, prunable combination of both. With over 4000 experimental configurations across 20 MONSTER datasets, 13 meth...
| Authors | Raphael Fischer, Angus Dempster, Sebastian Buschjäger et al. |
|---|---|
| Primary category | cs.LG |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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