EQRN_fit: EQRN fit function for independent data

In EQRN: Extreme Quantile Regression Neural Networks for Risk Forecasting

EQRN fit function for independent data

Description

Use the EQRN_fit_restart() wrapper instead, with data_type="iid", for better stability using fitting restart.

Usage

EQRN_fit(
  X,
  y,
  intermediate_quantiles,
  interm_lvl,
  shape_fixed = FALSE,
  net_structure = c(5, 3, 3),
  hidden_fct = torch::nnf_sigmoid,
  p_drop = 0,
  intermediate_q_feature = TRUE,
  learning_rate = 1e-04,
  L2_pen = 0,
  shape_penalty = 0,
  scale_features = TRUE,
  n_epochs = 500,
  batch_size = 256,
  X_valid = NULL,
  y_valid = NULL,
  quant_valid = NULL,
  lr_decay = 1,
  patience_decay = n_epochs,
  min_lr = 0,
  patience_stop = n_epochs,
  tol = 1e-06,
  orthogonal_gpd = TRUE,
  patience_lag = 1,
  optim_met = "adam",
  seed = NULL,
  verbose = 2,
  device = default_device()
)

Arguments

X	Matrix of covariates, for training.
y	Response variable vector to model the extreme conditional quantile of, for training.
intermediate_quantiles	Vector of intermediate conditional quantiles at level interm_lvl.
interm_lvl	Probability level for the intermediate quantiles intermediate_quantiles.
shape_fixed	Whether the shape estimate depends on the covariates or not (bool).
net_structure	Vector of integers whose length determines the number of layers in the neural network and entries the number of neurons in each corresponding successive layer. If hidden_fct=="SSNN", should instead be a named list with "scale" and "shape" vectors for the two respective sub-networks. Can also be a torch::nn_module network with correct input and output dimensions, which overrides the hidden_fct, shape_fixed and p_drop arguments.
hidden_fct	Activation function for the hidden layers. Can be either a callable function (preferably from the torch library), or one of the the strings "SNN", "SSNN" for self normalizing networks (with common or separated networks for the scale and shape estimates, respectively). In the latter cases, shape_fixed has no effect.
p_drop	Probability parameter for dropout before each hidden layer for regularization during training. alpha-dropout is used with SNNs.
intermediate_q_feature	Whether to use the intermediate_quantiles as an additional covariate, by appending it to the X matrix (bool).
learning_rate	Initial learning rate for the optimizer during training of the neural network.
L2_pen	L2 weight penalty parameter for regularization during training.
shape_penalty	Penalty parameter for the shape estimate, to potentially regularize its variation from the fixed prior estimate.
scale_features	Whether to rescale each input covariates to zero mean and unit variance before applying the network (recommended).
n_epochs	Number of training epochs.
batch_size	Batch size used during training.
X_valid	Covariates in a validation set, or NULL. Used for monitoring validation loss during training, enabling learning-rate decay and early stopping.
y_valid	Response variable in a validation set, or NULL. Used for monitoring validation loss during training, enabling learning-rate decay and early stopping.
quant_valid	Intermediate conditional quantiles at level interm_lvl in a validation set, or NULL. Used for monitoring validation loss during training, enabling learning-rate decay and early stopping.
lr_decay	Learning rate decay factor.
patience_decay	Number of epochs of non-improving validation loss before a learning-rate decay is performed.
min_lr	Minimum learning rate, under which no more decay is performed.
patience_stop	Number of epochs of non-improving validation loss before early stopping is performed.
tol	Tolerance for stopping training, in case of no significant training loss improvements.
orthogonal_gpd	Whether to use the orthogonal reparametrization of the estimated GPD parameters (recommended).
patience_lag	The validation loss is considered to be non-improving if it is larger than on any of the previous patience_lag epochs.
optim_met	DEPRECATED. Optimization algorithm to use during training. "adam" is the default.
seed	Integer random seed for reproducibility in network weight initialization.
verbose	Amount of information printed during training (0:nothing, 1:most important, 2:everything).
device	(optional) A torch::torch_device(). Defaults to default_device().

Value

An EQRN object of classes c("EQRN_iid", "EQRN"), containing the fitted network, as well as all the relevant information for its usage in other functions.

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