QRNN_RNN_net: Recurrent quantile regression neural network module
In EQRN: Extreme Quantile Regression Neural Networks for Risk Forecasting

QRNN_RNN_net

R Documentation

Recurrent quantile regression neural network module

A recurrent neural network as a torch::nn_module, designed for quantile regression.

QRNN_RNN_net(
  type = c("lstm", "gru"),
  nb_input_features,
  hidden_size,
  num_layers = 1,
  dropout = 0
)

`type`	the type of recurrent architecture, can be one of `"lstm"` (default) or `"gru"`,
`nb_input_features`	the input size (i.e. the number of features),
`hidden_size`	the dimension of the hidden latent state variables in the recurrent network,
`num_layers`	the number of recurrent layers,
`dropout`	probability parameter for dropout before each hidden layer for regularization during training.

The constructor allows specifying:

type: the type of recurrent architecture, can be one of "lstm" (default) or "gru",
nb_input_features: the input size (i.e. the number of features),
hidden_size: the dimension of the hidden latent state variables in the recurrent network,
num_layers: the number of recurrent layers,
dropout: probability parameter for dropout before each hidden layer for regularization during training.