通过剪枝提升时间序列分类能效，最高省电80%且精度损失低于5%
Pruning Extensions and Efficiency Trade-Offs for Sustainable Time Series Classification
- 对主流混合模型实施理论约束的剪枝策略，提出可剪枝新组合Hydrant
- 在20个数据集上实测显示剪枝后能耗降低80%，精度下降不足5%
- 提供可复现框架与工具，助力绿色高效时间序列分类实践
时间序列分类（TSC）具有重要应用价值，但缺乏对模型、数据集和硬件间性能与资源消耗权衡的统一理解。尽管资源意识增强，现有TSC方法尚未系统评估其能源效率。本文提出一个全面评估框架，明确探索预测性能与资源消耗之间的平衡。为提升效率，我们对领先混合分类器Hydra和Quant应用理论上受控的剪枝策略，并提出全新可剪枝组合Hydrant。在20个MONSTER数据集、13种方法及三种计算环境下，共进行4000余次实验配置，系统分析模型设计、超参数与硬件选择对实际性能的影响。结果表明，剪枝可使能耗最高降低80%，同时保持竞争力的预测质量，通常仅损失不到5%的准确率。所提方法、实验结果及配套软件推动了TSC向可持续、可复现方向发展。
原文摘要 · Abstract (English)
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.
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