TypiCore：一种用于时间序列类增量学习的混合主动查询策略
TypiCore: A Hybrid Active Query Strategy for Class-Incremental Learning on Time Series
- Budapest University of Technology and Economics（布达佩斯技术与经济大学）
- University of Padua（帕多瓦大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究多元时间序列的主动类增量学习，提出TypiCore混合查询策略，结合多种方法评估多种查询策略，揭示基于不确定性和分布感知方法局限性，该策略在多数据集上取得显著改进，减少标注需求。
AI中文摘要：
时间序列数据在众多领域起着关键作用，现实环境中模型需应对分布变化，现有持续学习方法面临标注成本高的问题。本文研究多元时间序列的主动类增量学习，结合多种基于排练的方法对多种查询策略进行系统评估，揭示了基于不确定性和分布感知方法在受限标注预算下的局限性。为此提出TypiCore，一种在主动学习周期中交替基于典型性和多样性样本选择的混合查询策略，在多个数据集上取得显著改进。
英文摘要：
Time series data play a pivotal role across numerous domains, including healthcare and manufacturing. In real-world environments, models must cope with distribution shifts over time, a challenge commonly addressed through Continual Learning (CL) techniques. However, existing CL methods face a critical limitation: real-world data streams are rarely fully labeled, making annotation cost a major practical constraint. This paper investigates Active Class-Incremental Learning (ACIL) for multivariate time series, where a model must sequentially learn new classes while selectively querying labels under a fixed annotation budget. We present a systematic evaluation of a wide range of query strategies combined with multiple rehearsal-based approaches, assessing their impact on plasticity, stability, and label efficiency across four benchmark datasets. Our analysis reveals the limitations of uncertainty-based and distribution-aware methods in achieving strong performance under constrained labeling budgets. To address these shortcomings, we propose TypiCore, a novel hybrid query strategy that alternates between typicality-based and diversity-based sample selection across active learning cycles, enabling the construction of memory buffers that are both representative and diverse. Evaluated on the TSCIL benchmark, TypiCore delivers significant improvements over all baselines and matches or surpasses fully supervised continual learning performance on multiple datasets while requiring a fraction of the available labels.
