process_features: Feature processor for EQRN

In EQRN: Extreme Quantile Regression Neural Networks for Risk Forecasting

Feature processor for EQRN

Description

Feature processor for EQRN

Usage

process_features(
  X,
  intermediate_q_feature,
  intermediate_quantiles = NULL,
  X_scaling = NULL,
  scale_features = TRUE
)

Arguments

X	A covariate matrix.
intermediate_q_feature	Whether to use the intermediate quantiles as an additional covariate, by appending it to the X matrix (bool).
intermediate_quantiles	The intermediate conditional quantiles.
X_scaling	Existing "X_scaling" object containing the precomputed mean and variance for each covariate. This enables reusing the scaling choice and parameters from the train set, if computing the excesses on a validation or test set, in order to avoid overfitting. This is performed automatically in the "EQRN" objects.
scale_features	Whether to rescale each input covariates to zero mean and unit variance before applying the network (recommended). If X_scaling is given, X_scaling$scaling overrides scale_features.

Value

Named list containing:

X_excesses	the (possibly rescaled and q_feat transformed) covariate matrix,
X_scaling	object of class "X_scaling" to use for consistent scaling on future datasets.

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