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(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 7982. #> 2 "ETS(log(value) ~ season(\"A\"))" 1 1979 Feb 7153. #> 3 "ETS(log(value) ~ season(\"A\"))" 1 1979 Mar 8034. #> 4 "ETS(log(value) ~ season(\"A\"))" 1 1979 Apr 8100. #> 5 "ETS(log(value) ~ season(\"A\"))" 1 1979 May 9106. #> 6 "ETS(log(value) ~ season(\"A\"))" 1 1979 Jun 9654. #> 7 "ETS(log(value) ~ season(\"A\"))" 1 1979 Jul 10059. #> 8 "ETS(log(value) ~ season(\"A\"))" 1 1979 Aug 9576. #> 9 "ETS(log(value) ~ season(\"A\"))" 1 1979 Sep 8312. #> 10 "ETS(log(value) ~ season(\"A\"))" 1 1979 Oct 8526. #> # … with 2,390 more rows