QRN_seq_predict_foldwise_sep: Sigle-fold foldwise fit-predict function using a recurrent...

In EQRN: Extreme Quantile Regression Neural Networks for Risk Forecasting

Sigle-fold foldwise fit-predict function using a recurrent QRN

Description

Separated single-fold version of QRN_seq_predict_foldwise(), for computation purposes.

Usage

QRN_seq_predict_foldwise_sep(
  X,
  y,
  q_level,
  n_folds = 3,
  fold_todo = 1,
  number_fits = 3,
  seq_len = 10,
  seed = NULL,
  ...
)

Arguments

X	Matrix of covariates, for training. Entries must be in sequential order.
y	Response variable vector to model the conditional quantile of, for training. Entries must be in sequential order.
q_level	Probability level of the desired conditional quantiles to predict.
n_folds	Number of folds.
fold_todo	Index of the fold to do (integer in 1:n_folds).
number_fits	Number of restarts, for stability.
seq_len	Data sequence length (i.e. number of past observations) used during training to predict each response quantile.
seed	Integer random seed for reproducibility in network weight initialization.
...	Other parameters given to QRN_seq_fit().

Value

A named list containing the foldwise predictions and fits. It namely contains:

predictions	the numerical vector of quantile predictions for each observation entry in y,
fits	a list containing the "QRN_seq" fitted networks for each fold,
cuts	the fold cuts indices,
folds	a list of lists containing the train indices, validation indices and fold separations as a list for each fold setup,
n_folds	number of folds,
q_level	probability level of the predicted quantiles,
train_losses	the vector of train losses on each fold,
valid_losses	the vector of validation losses on each fold,
min_valid_losses	the minimal validation losses obtained on each fold,
min_valid_e	the epoch index of the minimal validation losses obtained on each fold.

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