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

Examples

as_tsibble(Nile) %>% model(NAIVE(value)) %>% generate()
#> # A tsibble: 2 x 4 [1Y] #> # Key: .model, .rep [1] #> .model .rep index .sim #> <chr> <int> <dbl> <dbl> #> 1 NAIVE(value) 1 1971 926. #> 2 NAIVE(value) 1 1972 759.
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> <int> <qtr> <dbl> #> 1 snaive 1 2010 Q3 456. #> 2 snaive 1 2010 Q4 477. #> 3 snaive 1 2011 Q1 420. #> 4 snaive 1 2011 Q2 408. #> 5 snaive 1 2011 Q3 480. #> 6 snaive 1 2011 Q4 466. #> 7 snaive 1 2012 Q1 414. #> 8 snaive 1 2012 Q2 410.