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

arXiv: https://arxiv.org/abs/2510.17933 · HTML: https://mlsanity.com/paper/2510.17933/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 40 references that are in the map.
- First posted on arXiv 20 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [The frontier of simulation-based inference](https://mlsanity.com/paper/1911.01429/) (2019)
- [Automatic Posterior Transformation for Likelihood-Free Inference](https://mlsanity.com/paper/1905.07488/) (2019)
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- [Reducing Idleness in Financial Cloud Services via Multi-objective Evolutionary Reinforcement Learning based Load Balancer](https://mlsanity.com/paper/2305.03463/) (2023)
- [QuantFactor REINFORCE: Mining Steady Formulaic Alpha Factors with Variance-bounded REINFORCE](https://mlsanity.com/paper/2409.05144/) (2024)
- [It's Morphing Time: Unleashing the Potential of Multiple LLMs via Multi-objective Optimization](https://mlsanity.com/paper/2407.00487/) (2024)
- [Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms](https://mlsanity.com/paper/2306.02611/) (2023)
- [Learning from Expert Factors: Trajectory-level Reward Shaping for Formulaic Alpha Mining](https://mlsanity.com/paper/2507.20263/) (2025)
- [SimLOB: Learning Representations of Limited Order Book for Financial Market Simulation](https://mlsanity.com/paper/2406.19396/) (2024)
- [Towards Calibrating Financial Market Simulators with High-frequency Data](https://mlsanity.com/paper/2504.00538/) (2025)
- [Representation Learning of Limit Order Book: A Comprehensive Study and Benchmarking](https://mlsanity.com/paper/2505.02139/) (2025)
- [Surrogate-Assisted Evolutionary Reinforcement Learning Based on Autoencoder and Hyperbolic Neural Network](https://mlsanity.com/paper/2505.19423/) (2025)
- [From Flat to Hierarchical: Evolving Tree-structured Thoughts for Fine-grained Alpha Mining](https://mlsanity.com/paper/2508.16334/) (2025)
- [Detecting Multilevel Manipulation from Limit Order Book via Cascaded Contrastive Representation Learning](https://mlsanity.com/paper/2508.17086/) (2025)

## Abstract (arXiv)

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.