DeepL\'evy: Learning Heavy-Tailed Uncertainty in Highly Volatile Time Series

ArXi:2605.10364v1 Announce Type: new Modeling uncertainty in heavy-tailed time series remains a critical challenge for deep probabilistic forecasting models, which often struggle to capture abrupt, extreme events. While L\'evy stable distributions offer a natural framework for modeling such non-Gaussian behaviors, the intractability of their probability density functions severely limits conventional likelihood-based inference. To address this, we