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Flatline forecaster argument constructor

Source: R/flatline_forecaster.R
flatline_args_list.Rd

Constructs a list of arguments for flatline_forecaster().

Usage

flatline_args_list(
  ahead = 7L,
  n_training = Inf,
  forecast_date = NULL,
  target_date = NULL,
  quantile_levels = c(0.05, 0.95),
  symmetrize = TRUE,
  nonneg = TRUE,
  quantile_by_key = character(0L),
  ...
)

Arguments

ahead

Integer. Unlike arx_forecaster(), this doesn't have any effect on the predicted values. Predictions are always the most recent observation. However, this does impact the residuals stored in the object. Residuals are calculated based on this number to mimic how badly you would have done. So for example, ahead = 7 will create residuals by comparing values 7 days apart.

n_training

Integer. An upper limit for the number of rows per key that are used for training (in the time unit of the epi_df).

forecast_date

Date. The date from which the forecast is occurring. The default NULL will determine this automatically from either

  1. the maximum time value for which there's data if there is no latency adjustment (the default case), or

  2. the as_of date of epi_data if adjust_latency is non-NULL.

target_date

Date. The date that is being forecast. The default NULL will determine this automatically as forecast_date + ahead.

quantile_levels

Vector or NULL. A vector of probabilities to produce prediction intervals. These are created by computing the quantiles of training residuals. A NULL value will result in point forecasts only.

symmetrize

Logical. The default TRUE calculates symmetric prediction intervals. This argument only applies when residual quantiles are used. It is not applicable with trainer = quantile_reg(), for example.

nonneg

Logical. The default TRUE enforces nonnegative predictions by hard-thresholding at 0.

quantile_by_key

Character vector. Groups residuals by listed keys before calculating residual quantiles. See the by_key argument to layer_residual_quantiles() for more information. The default, character(0) performs no grouping. This argument only applies when residual quantiles are used. It is not applicable with trainer = quantile_reg(), for example.

...

Space to handle future expansions (unused).

Value

A list containing updated parameter choices with class flatline_alist.

Examples

flatline_args_list()
#> • ahead : 7
#> • n_training : Inf
#> • forecast_date : "NULL"
#> • target_date : "NULL"
#> • quantile_levels : 0.05 and 0.95
#> • symmetrize : TRUE
#> • nonneg : TRUE
#> • quantile_by_key : "_empty_"
flatline_args_list(symmetrize = FALSE)
#> • ahead : 7
#> • n_training : Inf
#> • forecast_date : "NULL"
#> • target_date : "NULL"
#> • quantile_levels : 0.05 and 0.95
#> • symmetrize : FALSE
#> • nonneg : TRUE
#> • quantile_by_key : "_empty_"
flatline_args_list(quantile_levels = c(.1, .3, .7, .9), n_training = 120)
#> • ahead : 7
#> • n_training : 120
#> • forecast_date : "NULL"
#> • target_date : "NULL"
#> • quantile_levels : 0.1, 0.3, 0.7, and 0.9
#> • symmetrize : TRUE
#> • nonneg : TRUE
#> • quantile_by_key : "_empty_"