Title: ChaoticFuzz: Fuzzy-Based Graph Representation for Spatiotemporal Learning
Primary area: learning on graphs and other geometries & topologies
Keywords: Time Series, Chaos Theory, Fuzzy Systems, Graph Neural Network

Abstract: Chaos theory addresses a unique class of dynamical systems that, despite operating under deterministic rules, exhibit extreme sensitivity to initial conditions, where even minor perturbations can lead to vastly different trajectories. Rather than modeling such systems directly in the time domain, chaotic time series are often reconstructed in higher-dimensional phase spaces, where latent structures such as attractors and fixed points emerge. However, forecasting within this context remains challenging, as conventional time series models are ill-suited to capture the complex interplay between spatial and temporal dynamics. In this work, we propose ChaoticFuzz, a novel framework that transforms univariate chaotic time series into graph-structured data by leveraging fuzzy clustering in phase space. Our framework encodes temporal trajectories and fuzzy membership degrees to construct weighted graphs, enabling the application of Graph Neural Networks for accurate long-term prediction. This graph-based representation preserves essential spatiotemporal patterns that are often lost in traditional approaches. Experiments on several benchmark chaotic systems demonstrate that ChaoticFuzz significantly outperforms state-of-the-art methods, which reflect a model’s ability to follow complex dynamical behavior.

TL;DR: We propose ChaoticFuzz, a fuzzy-logic-based method that transforms chaotic time series into graphs for improved prediction using Graph Neural Networks.