少样本下提前分类，通过智能选停时点提升预测准确率。
FETERS: Few-Shot Early Time-Series Classification via Effective Ratio Selection
- 用类留一法评估选择全局停时比率，无需训练额外停机模块。
- 5样本下平均调和均值达最优，38个数据集表现领先。
- 适合标注数据稀缺的早期时间序列分类任务。
早期时间序列分类（ETSC）旨在尽早从部分观测序列中做出准确预测。尽管已有多种停止机制与特征学习策略，但多数方法依赖充足标注数据，在标注受限场景中难以适用。在少样本条件下，训练样本级停止模块并提取有效分类特征均面临挑战。本文提出FETERS框架：通过支持集上类级别留一法（LOO）评估，选择数据集级停止比率，并采用基于惩罚的奖励函数平衡准确率与提前性，避免训练额外停止模块。FETERS结合Rocket特征与冻结的Chronos表示进行分类。在覆盖14个领域的69个公开数据集上实验表明，5样本设置下，FETERS达到当前最佳性能，平均调和均值最高，且在38个数据集上表现最优；在44个数据集上优于现有最先进方法。其在全样本设置下也保持竞争力，验证了对准确率-提前性权衡的有效管理。
原文摘要 · Abstract (English)
Early time-series classification (ETSC) aims to make accurate predictions from partially observed time series as early as possible. Although various stopping mechanisms and feature learning strategies have been developed for ETSC, most existing methods assume access to sufficient labeled training data, which may be unrealistic in applications with limited annotation. Under limited supervision, learning an additional sample-level stopping module and extracting effective classification features can both become challenging. In this paper, we propose FETERS, a few-shot ETSC framework that selects a dataset-level stopping ratio through class-wise leave-one-out (LOO) evaluation on the support set and uses a penalty-based reward function to manage the accuracy-earliness trade-off, thereby avoiding the need to train an additional stopping module. FETERS further combines Rocket-based features with frozen Chronos representations for classification. Extensive experiments on 69 public datasets spanning 14 domains show that FETERS achieves state-of-the-art (SOTA) performance in the 5-shot setting, with the highest average harmonic mean (HM) and the best HM on 38 datasets, while outperforming the current SOTA method on 44 datasets. FETERS also remains competitive in the full-shot setting, demonstrating its effectiveness in managing the accuracy-earliness trade-off.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。