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 ARIMA
generate(x, new_data, specials, bootstrap = FALSE, ...)
A fitted model.
A tsibble containing the time points and exogenous regressors to produce forecasts for.
(passed by fabletools::forecast.mdl_df()
).
If TRUE
, then forecast distributions are computed using simulation with resampled errors.
Other arguments passed to methods
fable_fit <- as_tsibble(USAccDeaths) %>%
model(model = ARIMA(value ~ 0 + pdq(0,1,1) + PDQ(0,1,1)))
fable_fit %>% generate(times = 10)
#> # A tsibble: 240 x 4 [1M]
#> # Key: .model, .rep [10]
#> .model .rep index .sim
#> <chr> <chr> <mth> <dbl>
#> 1 model 1 1979 Jan 8239.
#> 2 model 1 1979 Feb 8186.
#> 3 model 1 1979 Mar 8329.
#> 4 model 1 1979 Apr 8408.
#> 5 model 1 1979 May 8900.
#> 6 model 1 1979 Jun 9992.
#> 7 model 1 1979 Jul 11007.
#> 8 model 1 1979 Aug 10748.
#> 9 model 1 1979 Sep 9502.
#> 10 model 1 1979 Oct 10566.
#> # ℹ 230 more rows