Time Series Classification (TSC) encompasses two settings: classifying entire sequences or classifying segmented subsequences. The raw time series for segmented TSC usually contain Multiple classes with Varying Duration of each class (MVD). Therefore, the characteristics of MVD pose unique challenges for segmented TSC, yet have been largely overlooked by existing works. Specifically, there exists a natural temporal dependency between consecutive instances (segments) to be classified within MVD. However, mainstream TSC models rely on the assumption of independent and identically distributed (i.i.d.), focusing on independently modeling each segment. Additionally, annotators with varying expertise may provide inconsistent boundary labels, leading to unstable performance of noise-free TSC models. To address these challenges, we first formally demonstrate that valuable contextual information enhances the discriminative power of classification instances. Leveraging the contextual priors of MVD at both the data and label levels, we propose a novel consistency learning framework Con4m, which effectively utilizes contextual information more conducive to discriminating consecutive segments in segmented TSC tasks, while harmonizing inconsistent boundary labels for training. Extensive experiments across multiple datasets validate the effectiveness of Con4m in handling segmented TSC tasks on MVD.

翻译：时间序列分类（TSC）包含两种设定：对整个序列进行分类或对分段后的子序列进行分类。用于分段TSC的原始时间序列通常包含**多类别且各类别持续时间可变**（MVD）的特性。因此，MVD的特性给分段TSC带来了独特的挑战，但现有研究在很大程度上忽视了这一点。具体而言，在MVD中待分类的连续实例（片段）之间存在天然的时序依赖关系。然而，主流TSC模型依赖于独立同分布（i.i.d.）假设，侧重于对每个片段进行独立建模。此外，具有不同专业知识的标注者可能提供不一致的边界标签，导致对噪声敏感的TSC模型性能不稳定。为应对这些挑战，我们首先从形式上证明有价值的上下文信息能够增强分类实例的判别能力。利用MVD在数据和标签层面的上下文先验，我们提出了一种新颖的一致性学习框架Con4m。该框架能有效利用更有利于区分连续片段的上下文信息来处理分段TSC任务，同时协调训练中不一致的边界标签。在多个数据集上的大量实验验证了Con4m在处理MVD分段TSC任务上的有效性。