3 papers
cs.CE2026
Dynamic Hypergraph Representation Learning for Multivariate Time Series without Prior Knowledge
Marco Gregnanin, Johannes De Smedt, Giorgio Gnecco +1
Hypergraphs have the capacity to capture higher-dimensional relationships among entities across various domains, making them a subject of growing interest within the research commu…
cs.CE2026
A Generative Adversarial Graph Neural Network for Synthetic Time Series Data
Marco Gregnanin, Johannes De Smedt, Giorgio Gnecco +1
Generating synthetic data for financial time series poses challenges, especially considering their non-stationary nature. Traditional statistical time series models normally assume…
cs.CE2026
The Statistical Significance of the Inclusion of Graph Neural Networks in the Financial Time Series Forecasting Problem
Marco Gregnanin, Johannes De Smedt, Giorgio Gnecco +1
Forecasting univariate time series in the financial market is a challenging endeavor. While numerous statistical and machine learning models have been introduced to address this ch…