用形状模式加速时间序列数据压缩，速度提升万倍且更准确。
ShapeCond: Fast Shapelet-Guided Dataset Condensation for Time Series Classification
- 基于形状模式引导优化，显式保留局部关键特征
- 合成速度比现有方法快29倍，最长可提速10000倍
- 适合需要高效训练的时序分类场景，如睡眠分析
时间序列数据广泛应用于金融、气候科学等领域，但其快速增长对存储和计算带来压力。数据压缩可通过生成紧凑的训练集缓解此问题，但多数方法聚焦图像，难以捕捉时间序列特有的时序结构，尤其忽略局部判别性模式（如形状模式）。本文提出ShapeCond，一种新型高效的时间序列分类数据压缩框架，通过形状模式引导的优化策略，利用形状模式知识实现数据合成。该方法的合成成本与序列长度无关：序列越长，加速越明显——相比最新方法CondTSC快29倍，在包含3000个时间步的Sleep数据集上甚至快达10000倍。通过显式保留关键局部模式，ShapeCond在下游任务中显著提升准确率，且在广泛实验中持续优于所有现有先进方法。代码已公开于https://github.com/lunaaa95/ShapeCond。
原文摘要 · Abstract (English)
Time series data supports many domains (e.g., finance and climate science), but its rapid growth strains storage and computation. Dataset condensation can alleviate this by synthesizing a compact training set that preserves key information. Yet most condensation methods are image-centric and often fail on time series because they miss time-series-specific temporal structure, especially local discriminative motifs such as shapelets. In this work, we propose ShapeCond, a novel and efficient condensation framework for time series classification that leverages shapelet-based dataset knowledge via a shapelet-guided optimization strategy. Our shapelet-assisted synthesis cost is independent of sequence length: longer series yield larger speedups in synthesis (e.g., 29$\times$ faster over prior state-of-the-art method CondTSC for time-series condensation, and up to 10,000$\times$ over naively using shapelets on the Sleep dataset with 3,000 timesteps). By explicitly preserving critical local patterns, ShapeCond improves downstream accuracy and consistently outperforms all prior state-of-the-art time series dataset condensation methods across extensive experiments. Code is available at https://github.com/lunaaa95/ShapeCond.
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