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 future information used to forecast.
bootstrap	If TRUE, then forecast distributions are computed using simulation with resampled errors.
...	Additional arguments for forecast model 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       index .rep   .sim
#>   <chr>        <dbl> <chr> <dbl>
#> 1 NAIVE(value)  1971 1      685.
#> 2 NAIVE(value)  1972 1      881.

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