Foldwise fit-predict function using a recurrent QRN

Foldwise fit-predict function using a recurrent QRN

Usage

QRN_seq_predict_foldwise(
  X,
  y,
  q_level,
  n_folds = 3,
  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.

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:

• predictionsthe numerical vector of quantile predictions for each observation entry in y,

• fitsa list containing the "QRN_seq" fitted networks for each fold,

• cutsthe fold cuts indices,

• foldsa list of lists containing the train indices, validation indices and fold separations as a list for each fold setup,

• n_foldsnumber of folds,

• q_levelprobability level of the predicted quantiles,

• train_lossesthe vector of train losses on each fold,

• valid_lossesthe vector of validation losses on each fold,

• min_valid_lossesthe minimal validation losses obtained on each fold,

• min_valid_ethe epoch index of the minimal validation losses obtained on each fold.