EQRN fit function for sequential and time series data

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

Usage

EQRN_fit_seq(
  X,
  y,
  intermediate_quantiles,
  interm_lvl,
  shape_fixed = FALSE,
  hidden_size = 10,
  num_layers = 1,
  rnn_type = c("lstm", "gru"),
  p_drop = 0,
  intermediate_q_feature = TRUE,
  learning_rate = 1e-04,
  L2_pen = 0,
  seq_len = 10,
  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-05,
  orthogonal_gpd = TRUE,
  patience_lag = 1,
  fold_separation = NULL,
  optim_met = "adam",
  seed = NULL,
  verbose = 2,
  device = default_device()
)

Arguments

X: Matrix of covariates, for training. Entries must be in sequential order.
y: Response variable vector to model the extreme conditional quantile of, for training. Entries must be in sequential order.
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).
hidden_size: Dimension of the hidden latent state variables in the recurrent network.
num_layers: Number of recurrent layers.
rnn_type: Type of recurrent architecture, can be one of "lstm" (default) or "gru".
p_drop: Probability parameter for dropout before each hidden layer for regularization during training.
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.
seq_len: Data sequence length (i.e. number of past observations) used during training to predict each response quantile.
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 covariance 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. Entries must be in sequential order. Used for monitoring validation loss during training, enabling learning-rate decay and early stopping.
y_valid: Response variable in a validation set, or NULL. Entries must be in sequential order. 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.
fold_separation: Index of fold separation or sequential discontinuity in the data.
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_seq", "EQRN"), containing the fitted network, as well as all the relevant information for its usage in other functions.