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 ETS generate(x, new_data, specials, bootstrap = FALSE, ...)
x | A fitted model. |
---|---|
new_data | A |
specials | (passed by |
bootstrap | If |
... | Additional arguments for forecast model methods. |
#> # A tsibble: 2,400 x 4 [1M] #> # Key: .model, .rep [100] #> .model index .rep .sim #> <chr> <mth> <chr> <dbl> #> 1 "ETS(log(value) ~ season(\"A\"))" 1979 Jan 1 8540. #> 2 "ETS(log(value) ~ season(\"A\"))" 1979 Feb 1 7699. #> 3 "ETS(log(value) ~ season(\"A\"))" 1979 Mar 1 8701. #> 4 "ETS(log(value) ~ season(\"A\"))" 1979 Apr 1 8844. #> 5 "ETS(log(value) ~ season(\"A\"))" 1979 May 1 10054. #> 6 "ETS(log(value) ~ season(\"A\"))" 1979 Jun 1 10151. #> 7 "ETS(log(value) ~ season(\"A\"))" 1979 Jul 1 10225. #> 8 "ETS(log(value) ~ season(\"A\"))" 1979 Aug 1 10220. #> 9 "ETS(log(value) ~ season(\"A\"))" 1979 Sep 1 8826. #> 10 "ETS(log(value) ~ season(\"A\"))" 1979 Oct 1 8670. #> # … with 2,390 more rows