EQRN-package: EQRN: Extreme Quantile Regression Neural Networks for Risk...

In EQRN: Extreme Quantile Regression Neural Networks for Risk Forecasting

EQRN: Extreme Quantile Regression Neural Networks for Risk Forecasting

Description

This framework enables forecasting and extrapolating measures of conditional risk (e.g. of extreme or unprecedented events), including quantiles and exceedance probabilities, using extreme value statistics and flexible neural network architectures. It allows for capturing complex multivariate dependencies, including dependencies between observations, such as sequential dependence (time-series). The methodology was introduced in Pasche and Engelke (2024) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/24-AOAS1907")} (also available in preprint: Pasche and Engelke (2022) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2208.07590")}).

Author(s)

Maintainer: Olivier C. Pasche [e-mail] (ORCID) [copyright holder]

See Also

Useful links:

• https://github.com/opasche/EQRN

• https://opasche.github.io/EQRN/

• Report bugs at https://github.com/opasche/EQRN/issues

EQRN documentation built on April 4, 2025, 12:45 a.m.