mts_dataset: Dataset creator for sequential data
In EQRN: Extreme Quantile Regression Neural Networks for Risk Forecasting

mts_dataset

R Documentation

Dataset creator for sequential data

Description

A torch::dataset object that can be initialized with sequential data, used to feed a recurrent network during training or prediction. It 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. It can perform scaling of the response's past as a covariate, and compute excesses as a response when used in EQRN_fit_seq(). It 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 = EQRN::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 `0` and `1`. If `sample_frac < 1`, a subsample of the data is used. Defaults to `1`.
`device`	(optional) A `torch::torch_device()`. Defaults to `default_device()`.