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From Observations to Parameters Detecting Changepoint in Nonlinear Dynamics with Simulation-based Inference

X Deng, C Chen, P Yang - arXiv preprint arXiv:2510.17933, 2025 - arxiv.org
Computer Science paper cs.LG Suggest

… The first stage consists of an offline training phase, where we use simulation-based inference to train a neural network that learns to estimate the posterior distribution of …

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BibTeX

@article{2510.17933v1,
Author        = {Xiangbo Deng and Cheng Chen and Peng Yang},
Title         = {From Observations to Parameters: Detecting Changepoint in Nonlinear Dynamics with Simulation-based Inference},
Eprint        = {2510.17933v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.LG},
Abstract      = {Detecting regime shifts in chaotic time series is hard because observation-space signals are entangled with intrinsic variability. We propose Parameter--Space Changepoint Detection (Param--CPD), a two--stage framework that first amortizes Bayesian inference of governing parameters with a neural posterior estimator trained by simulation-based inference, and then applies a standard CPD algorithm to the resulting parameter trajectory. On Lorenz--63 with piecewise-constant parameters, Param--CPD improves F1, reduces localization error, and lowers false positives compared to observation--space baselines. We further verify identifiability and calibration of the inferred posteriors on stationary trajectories, explaining why parameter space offers a cleaner detection signal. Robustness analyses over tolerance, window length, and noise indicate consistent gains. Our results show that operating in a physically interpretable parameter space enables accurate and interpretable changepoint detection in nonlinear dynamical systems.},
Year          = {2025},
Month         = {Oct},
Url           = {http://arxiv.org/abs/2510.17933v1},
File          = {2510.17933v1.pdf}
}

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