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 model_mean
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(USAccDeaths) %>% model(ETS(log(value) ~ season("A"))) %>% generate(times = 100)
#> # A tsibble: 2,400 x 4 [1M] #> # Key: .model, .rep [100] #> .model .rep index .sim #> <chr> <int> <mth> <dbl> #> 1 "ETS(log(value) ~ season(\"A\"))" 1 1979 Jan 8626. #> 2 "ETS(log(value) ~ season(\"A\"))" 1 1979 Feb 7583. #> 3 "ETS(log(value) ~ season(\"A\"))" 1 1979 Mar 8621. #> 4 "ETS(log(value) ~ season(\"A\"))" 1 1979 Apr 8998. #> 5 "ETS(log(value) ~ season(\"A\"))" 1 1979 May 9888. #> 6 "ETS(log(value) ~ season(\"A\"))" 1 1979 Jun 10745. #> 7 "ETS(log(value) ~ season(\"A\"))" 1 1979 Jul 11251. #> 8 "ETS(log(value) ~ season(\"A\"))" 1 1979 Aug 10910. #> 9 "ETS(log(value) ~ season(\"A\"))" 1 1979 Sep 9007. #> 10 "ETS(log(value) ~ season(\"A\"))" 1 1979 Oct 9574. #> # … with 2,390 more rows