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

2025-09-04

AI Summary

This study addresses the detection of “flickering”—stochastic switching between coexisting stable states under noise—as an early indicator of declining resilience in complex systems (e.g., climate, ecological, and financial systems). We propose the first deep learning–based framework for flickering detection: a convolutional long short-term memory (CNN-LSTM) network trained on diverse synthetic time series generated from polynomial dynamical systems with additive noise. The model enables end-to-end identification of flickering events without hand-crafted features. It exhibits strong generalizability, successfully transferring to real-world datasets—including North American deer mouse body temperature dynamics and African humid period paleoclimatic proxy records—to robustly capture pre-critical flickering signatures. This work pioneers the application of deep learning to flickering detection, establishing an interpretable, data-driven early-warning paradigm for systemic instability across disciplines.

Technology Category

• Machine Learning: Deep Learning Theory
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Detecting flickering in complex systems using deep learning
• Identifying early warning signals before critical regime shifts
• Generalizing detection to diverse stochastic and empirical systems

Innovation

• CNN LSTM models detect flickering patterns
• Trained on synthetic noisy time series
• Generalizes to diverse stochastic systems