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Found 4 papers, 3 papers with code
Extreme Conformal Prediction: Reliable Intervals for High-Impact Events
The advantages of this extreme conformal prediction method are illustrated in a simulation study and in an application to flood risk forecasting.
Granger Causality in Extremes
We introduce a rigorous mathematical framework for Granger causality in extremes, designed to identify causal links from extreme events in time series.
Validating Deep Learning Weather Forecast Models on Recent High-Impact Extreme Events
To address these issues, we compare ML weather prediction models (GraphCast, PanguWeather, and FourCastNet) and ECMWF's high-resolution forecast system (HRES) in three case studies: the 2021 Pacific Northwest heatwave, the 2023 South Asian humid heatwave, and the North American winter storm in 2021.
Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood Risk
We propose the EQRN model that combines tools from neural networks and extreme value theory into a method capable of extrapolation in the presence of complex predictor dependence.
