# Echoes Before Collapse: Deep Learning Detection of Flickering in Complex Systems

arXiv: https://arxiv.org/abs/2509.04683 · HTML: https://mlsanity.com/paper/2509.04683/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 53 references that are in the map.
- First posted on arXiv 4 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [Social physics](https://mlsanity.com/paper/2110.01866/) (2021)
- [Predicting discrete-time bifurcations with deep learning](https://mlsanity.com/paper/2303.09669/) (2023)
- [Universal Early Warning Signals of Phase Transitions in Climate Systems](https://mlsanity.com/paper/2206.00060/) (2022)
- [Deep Learning for predicting rate-induced tipping](https://mlsanity.com/paper/2409.07590/) (2024)
- [Statistical physics of vaccination](https://mlsanity.com/paper/1608.09010/) (2016)
- [Tipping Points of Evolving Epidemiological Networks: Machine Learning-Assisted, Data-Driven Effective Modeling](https://mlsanity.com/paper/2311.00797/) (2023)
- [Neural models for prediction of spatially patterned phase transitions: methods and challenges](https://mlsanity.com/paper/2505.09718/) (2025)

## Abstract (arXiv)

Deep learning offers powerful tools for anticipating tipping points in complex systems, yet its potential for detecting flickering (noise-driven switching between coexisting stable states) remains unexplored. Flickering is a hallmark of reduced resilience in climate systems, ecosystems, financial markets, and other systems. It can precede critical regime shifts that are highly impactful but difficult to predict. Here we show that convolutional long short-term memory (CNN LSTM) models, trained on synthetic time series generated from simple polynomial functions with additive noise, can accurately identify flickering patterns. Despite being trained on simplified dynamics, our models generalize to diverse stochastic systems and reliably detect flickering in empirical datasets, including dormouse body temperature records and palaeoclimate proxies from the African Humid Period. These findings demonstrate that deep learning can extract early warning signals from noisy, nonlinear time series, providing a flexible framework for identifying instability across a wide range of dynamical systems.