A torch::dataset object that can be initialized with sequential data,
used to feed a recurrent network during training or prediction.
Is used in EQRN_fit_seq() and corresponding predict functions,
as well as in other recurrent methods such as QRN_seq_fit() and its predict functions.
Can perform scaling of the response's past as a covariate, and compute excesses as a response when used in EQRN_fit_seq().
Also allows for fold separation or sequential discontinuity in the data.
Usage
mts_dataset(
Y,
X,
seq_len,
intermediate_quantiles = NULL,
scale_Y = TRUE,
fold_separation = NULL,
sample_frac = 1,
device = default_device()
)Arguments
- Y
Response variable vector to model the extreme conditional quantile of, for training. Entries must be in sequential order.
- X
Matrix of covariates, for training. Entries must be in sequential order.
- seq_len
Data sequence length (i.e. number of past observations) used during training to predict each response quantile.
- intermediate_quantiles
Vector of intermediate conditional quantiles at level
interm_lvl.- scale_Y
Whether to rescale the response past, when considered as an input covariate, to zero mean and unit covariance before applying the network (recommended).
- fold_separation
Fold separation index, when using concatenated folds as data.
- sample_frac
Value between
0and1. Ifsample_frac < 1, a subsample of the data is used. Defaults to1.- device
(optional) A
torch::torch_device(). Defaults todefault_device().