Generate new data from a fable model

Simulates future paths from a dataset using a fitted model. Innovations are sampled by the model's assumed error distribution. If bootstrap is TRUE, innovations will be sampled from the model's residuals. If new_data contains the .innov column, those values will be treated as innovations.

# S3 method for RW
generate(x, new_data, bootstrap = FALSE, ...)

Arguments

x

A fitted model.

new_data

A tsibble containing the time points and exogenous regressors to produce forecasts for.

bootstrap

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

...

Other arguments passed to methods

See also

fabletools::generate.mdl_df

Examples

as_tsibble(Nile) %>%
  model(NAIVE(value)) %>%
  generate()
#> # A tsibble: 2 x 4 [1Y]
#> # Key:       .model, .rep [1]
#>   .model       .rep  index  .sim
#>   <chr>        <chr> <dbl> <dbl>
#> 1 NAIVE(value) 1      1971  685.
#> 2 NAIVE(value) 1      1972  881.

library(tsibbledata)
aus_production %>%
  model(snaive = SNAIVE(Beer ~ lag("year"))) %>%
  generate()
#> # A tsibble: 8 x 4 [1Q]
#> # Key:       .model, .rep [1]
#>   .model .rep  Quarter  .sim
#>   <chr>  <chr>   <qtr> <dbl>
#> 1 snaive 1     2010 Q3  402.
#> 2 snaive 1     2010 Q4  507.
#> 3 snaive 1     2011 Q1  412.
#> 4 snaive 1     2011 Q2  346.
#> 5 snaive 1     2011 Q3  402.
#> 6 snaive 1     2011 Q4  466.
#> 7 snaive 1     2012 Q1  443.
#> 8 snaive 1     2012 Q2  357.