Multivariate de Bruijn Graphs: A Symbolic Graph Framework for Time Series Forecasting

The study introduces DRAGON, an encoder that uses Multivariate de Bruijn Graphs to discretize and represent continuous time series data structurally, facilitating better neural modeling. By integrating graph-based attention into a dual-branch architecture, DRAGON enhances traditional CNN encoders with symbolic, structure-aware features to improve forecasting accuracy.