Topology-Aware and Interpretable Cryptocurrency Price Forecasting via Visibility Graphs and Ensemble Models

Abstract

Cryptocurrency markets represent highly complex and adaptive systems with non-linear and multi-scale dynamics, making them ideal for analysis through the lens of network science and statistical mechanics. This study introduces a robust network-oriented framework for forecasting cryptocurrency price dynamics, leveraging visibility graph-based reconstructions to capture temporal structures and dependencies in market data. Key network features, including centrality measures, community structure, local efficiency, and eccentricity, are extracted to characterize the underlying system's topology. These network metrics are incorporated into interpretable ensemble learning models, specifically Random Forest and LightGBM, to yield high predictive accuracy while elucidating the critical role of network topology in shaping cryptocurrency price evolution. To ensure interpretability in financial decision-making, this study uses Local Interpretable Model-agnostic Explanations (LIME) to reveal how key network metrics, such as community-based metrics and local efficiency, drive price predictions, providing transparency and reliability in high-risk markets. By bridging network science and econophysics, this work provides a novel and transparent approach to modeling cryptocurrency markets, offering unique perspectives into forecasting challenges, feature significance, and the interplay between structural patterns and market behavior. The findings contribute to advancing the understanding of cryptocurrencies as complex systems within the global financial ecosystem, reinforcing their transformative role in economic dynamics.