Pruning Extensions and Efficiency Trade-Offs for Sustainable Time Series Classification
Authors: Raphael Fischer, Angus Dempster, Sebastian Buschjäger, Matthias Jakobs, Urav Maniar, Geoffrey I. Webb
Organizations: Lamarr Institute for ML and AI, TU Dortmund University, Dortmund, Germany · Department of Data Science & AI, Monash University, Melbourne, Australia · Department of Pulmonary Medicine, University Medicine Essen, Essen, Germany · Faculty of Information Technology, Monash University, Melbourne, Australia
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 methods, and three compute setups, we systematically analyze how model design, hyperparameters, and hardware choices affect practical TSC performance. Our results showcase that pruning can significantly reduce energy consumption by up to 80% while maintaining competitive predictive quality, usually costing the model less than 5% of accuracy. The proposed methodology, experimental results, and accompanying software advance TSC toward sustainable and reproducible practice.
Figures & tables
Figure 1: Energy ( x ) versus accuracy ( y ) performance of TS classifiers when deployed for inference on two processors (left / right), demonstrating intricate trade-offs [ 16 ] .
Figure 2: Performance statistics and trade-offs among TSC models ( y -axis) across datasets, assessed along four evaluation dimensions (columns) on the Intel i9-13900 .
Figure 3: Critical differences [ 12 ] among original and pruned hybrids, showing how energy draw and compound performance are significantly improved (higher ranks).
Figure 4: Pruning rate ablation study on model quality (left) and linearly decreasing energy demand (right), averaged across all datasets (thin lines).
Figure 5: Impacts on model energy draw across all datasets when using the i7 CPU (left) or GPU (middle) instead of the i9 , or varying the optimal batch size (right).