arXiv · 2603.27395
Topological Detection of Hopf Bifurcations via Persistent Homology: A Functional Criterion from Time Series
Abstract
We propose a topological framework for detecting Hopf-type dynamical transitions directly from scalar time series. The method combines delay-coordinate reconstruction with persistent homology and uses the maximum persistence of one-dimensional homology classes as a scalar descriptor of cyclic structure. For the supercritical Hopf setting, we derive finite-resolution persistence bounds that relate detectability of the reconstructed periodic orbit to its geometry, sampling quality, and finite-data perturbations. A derivative-based estimator is then introduced to localize the critical parameter from the sampled topological functional. The approach is evaluated on the Hopf normal form, the Lorenz system, and a reduced Belousov--Zhabotinsky model. The numerical experiments show accurate finite-resolution localization of the corresponding transitions and illustrate the effects of embedding parameters, temporal sampling, smoothing, observational noise, and transient removal. These results support persistent homology as an interpretable data-driven tool for detecting geometric reorganizations in nonlinear time series.
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Jhonathan Barrios, Yásser Echávez, Carlos F. Álvarez. 2026-03-28. Topological Detection of Hopf Bifurcations via Persistent Homology: A Functional Criterion from Time Series. https://arxiv.org/abs/2603.27395
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