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

• URL: http://arxiv.org/abs/2510.17933v1
• Date: Mon, 20 Oct 2025 15:29:31 GMT
• Title: From Observations to Parameters: Detecting Changepoint in Nonlinear Dynamics with Simulation-based Inference
• Abstract summary: Param--CPD is a two-stage framework that first amortizes Bayesian inference of governing parameters with a neural posterior estimator trained by simulation-based inference.<n>We show that operating in a physically interpretable parameter space enables accurate and interpretable changepoint detection in nonlinear dynamical systems.
• Score: 6.621511814416357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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