Generate new data from a fable model

Source: R/arima.R

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, ...)

Arguments

x

A fitted model.
new_data

A tsibble containing future information used to forecast.
specials

(passed by fabletools::forecast.mdl_df()).
bootstrap

If TRUE, then forecast distributions are computed using simulation with resampled errors.
...

Additional arguments for forecast model methods.

See also

fabletools::generate.mdl_df

Examples

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    index .rep    .sim
#>    <chr>     <mth> <chr>  <dbl>
#>  1 model  1979 Jan 1      8239.
#>  2 model  1979 Feb 1      8186.
#>  3 model  1979 Mar 1      8329.
#>  4 model  1979 Apr 1      8408.
#>  5 model  1979 May 1      8900.
#>  6 model  1979 Jun 1      9992.
#>  7 model  1979 Jul 1     11007.
#>  8 model  1979 Aug 1     10748.
#>  9 model  1979 Sep 1      9502.
#> 10 model  1979 Oct 1     10566.
#> # … with 230 more rows