Vector visibility graph for rare event classification in complex system multivariate time series data

Rare event classification in multivariate time series is a critical yet challenging task across different industries. Traditional methods often struggle to capture the nonlinear and nonstationary dynamics inherent in complex multivariate time series data, limiting their ability to accurately detect rare events. To address these limitations, this study introduces the Vector Visibility Graph-based Graph Attention Network (VVG-GAT) framework, a novel approach that leverages the power of Vector Visibility Graphs (VVG) to represent multivariate time series as directed, weighted graphs. By encoding both temporal and inter-variable dependencies, the VVG facilitates the application of advanced graph neural networks, particularly the Graph Attention Network (GAT), to address the challenges of rare event classification and early detection. The proposed framework was evaluated on a real-world case study involving a pulp-and-paper manufacturing process characterized by rare paper break events. Experimental results demonstrated that the VVG-GAT framework significantly outperformed traditional models across several metrics. The study further highlights the potential of incorporating VVG-derived network statistics as additional features for machine learning and deep learning models. The VVG-GAT framework represents a significant advancement in rare event classification for multivariate time series generated from complex systems, providing a new solution with broad applicability across various domains.