Time series prototype learning is fundamentally challenged by observational ambiguity. Discrete architectures fail to resolve this, as they lack the capacity to decouple stochastic noise from continuous dynamics. Furthermore, rigid closed-set assumptions fail to capture unseen diversity. To address these limitations, we propose a hierarchical ordinary differential equation clustering network, which utilizes neural ordinary differential equation to model latent state evolution as a continuous integral curve. This formulation enforces temporal continuity to effectively disentangle smooth feature trends from stochastic noise, while our adaptive hierarchical mechanism autonomously determines the appropriate number of prototypes without rigid prior constraints. Validated on the early link failure detection task with irregularly sampled time series, the proposed method effectively extracts underlying physical prototypes, thereby enabling robust failure detection. Our code is available at https://github.com/NJ-LNN/Hierarchical-ODE.

翻译：时间序列原型学习面临观测模糊性的根本挑战。离散架构因缺乏将随机噪声与连续动力学解耦的能力而无法解决这一问题。此外，刚性封闭集合假设无法捕捉未观测到的多样性。针对这些局限，我们提出一种层次化常微分方程聚类网络，利用神经常微分方程将隐状态演化建模为连续积分曲线。该公式通过强制时间连续性有效分离平滑特征趋势与随机噪声，同时自适应层次机制无需刚性先验约束即可自主确定原型数量。在不规则采样时间序列的早期链路故障检测任务验证中，所提方法有效提取了底层物理原型，从而实现鲁棒的故障检测。我们的代码开源在 https://github.com/NJ-LNN/Hierarchical-ODE。