3 papers
cs.AI2026
Localized Anomaly Detection via Differentiable D-vine Copulas
Nicholas Andrea Pearson, Francesca Zanello, Davide Russo +2
Vine copulas provide a flexible framework for modeling complex multivariate distributions through a hierarchical decomposition into bivariate pair-copulas. Fitting a D-vine require…
cs.LG2025
CoCAI: Copula-based Conformal Anomaly Identification for Multivariate Time-Series
Nicholas A. Pearson, Francesca Zanello, Davide Russo +2
We propose a novel framework that harnesses the power of generative artificial intelligence and copula-based modeling to address two critical challenges in multivariate time-series…
cs.LG2025
Diffusion-based Time Series Forecasting for Sewerage Systems
Nicholas A. Pearson, Francesca Cairoli, Luca Bortolussi +2
We introduce a novel deep learning approach that harnesses the power of generative artificial intelligence to enhance the accuracy of contextual forecasting in sewerage systems. By…