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This research introduces a hierarchical neural ODE architecture designed to disentangle stochastic noise from continuous dynamics in irregular time series data, specifically for early link failure detection. By modeling latent state evolution as integral curves without rigid closed-set assumptions, the method autonomously identifies physical prototypes that discrete architectures typically miss. This advancement addresses critical limitations in observational ambiguity within machine learning systems handling real-world sensor or network telemetry.
Hierarchical ODE: Learning Continuous-Time Physical Prototypes for Early Link Failure Detection
arXiv:2606.14284v1 Announce Type: cross Abstract: 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.
Abstract: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 this https URL.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.14284 [cs.LG]
(or arXiv:2606.14284v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.14284 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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