Generate responses from a mable

Use a fitted model to simulate future data with similar behaviour to the response.

Usage

# S3 method for class 'mdl_vtl_df'
generate(x, new_data = NULL, h = NULL, bootstrap = FALSE, times = 1, ...)

Arguments

x

A mable.

new_data

Future data needed for generation (should include the time index and exogenous regressors)

h

The simulation horizon (can be used instead of new_data for regular time series with no exogenous regressors).

bootstrap

If TRUE, then forecast distributions are computed using simulation with resampled errors.

times

The number of replications.

...

Additional arguments

Value

A vital object with simulated values.

Details

Innovations are sampled by the model's assumed error distribution. If bootstrap is TRUE, innovations will be sampled from the model's residuals.

Author

Rob J Hyndman and Mitchell O'Hara-Wild

Examples

norway_mortality |>
  model(lc = LC(Mortality)) |>
  generate(times = 3, bootstrap = TRUE)
#> Warning: 3 errors (1 unique) encountered for lc
#> [3] Lee-Carter models require a log transformation of the response variable.
#> # A vital: 1,998 x 6 [1Y]
#> # Key:     Age x (Sex, .model, .rep) [111 x 9]
#>     Year   Age Sex    .model .rep   .sim
#>    <dbl> <int> <chr>  <chr>  <chr> <dbl>
#>  1  2024     0 Female lc     1        NA
#>  2  2024     1 Female lc     1        NA
#>  3  2024     2 Female lc     1        NA
#>  4  2024     3 Female lc     1        NA
#>  5  2024     4 Female lc     1        NA
#>  6  2024     5 Female lc     1        NA
#>  7  2024     6 Female lc     1        NA
#>  8  2024     7 Female lc     1        NA
#>  9  2024     8 Female lc     1        NA
#> 10  2024     9 Female lc     1        NA
#> # ℹ 1,988 more rows