4 papers
Extreme Conformal Prediction: Reliable Intervals for High-Impact Events
Olivier C. Pasche, Henry Lam, Sebastian Engelke
Conformal prediction is a popular method to construct prediction intervals with marginal coverage guarantees from black-box machine learning models. In applications with potentiall…
Granger Causality in Extremes
Juraj Bodik, Olivier C. Pasche
We introduce a rigorous mathematical framework for Granger causality in extremes, designed to identify causal links from extreme events in time series. Granger causality plays a pi…
Validating Deep Learning Weather Forecast Models on Recent High-Impact Extreme Events
Olivier C. Pasche, Jonathan Wider, Zhongwei Zhang +2
The forecast accuracy of machine learning (ML) weather prediction models is improving rapidly, leading many to speak of a "second revolution in weather forecasting". With numerous…
Modeling Extreme Events: Univariate and Multivariate Data-Driven Approaches
Gloria Buriticá, Manuel Hentschel, Olivier C. Pasche +2
This article summarizes the contribution of team genEVA to the EVA (2023) Conference Data Challenge. The challenge comprises four individual tasks, with two focused on univariate e…