Echoes Before Collapse: Deep Learning Detection of Flickering in Complex Systems
This provides a flexible deep learning framework for identifying instability in diverse dynamical systems, addressing a key challenge in predicting impactful regime shifts.
The paper tackled the problem of detecting flickering, a precursor to critical regime shifts in complex systems like climate and ecosystems, by showing that CNN LSTM models trained on synthetic data can accurately identify flickering patterns in empirical datasets, achieving reliable detection in cases such as dormouse body temperature records.
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.