Forecast a model from the fable package

Produces forecasts from a trained model.

# S3 method for ETS
forecast(
  object,
  new_data,
  specials = NULL,
  simulate = FALSE,
  bootstrap = FALSE,
  times = 5000,
  ...
)

Arguments

object

A model for which forecasts are required.

new_data

A tsibble containing the time points and exogenous regressors to produce forecasts for.

specials

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

simulate

If TRUE, prediction intervals are produced by simulation rather than using analytic formulae.

bootstrap

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

times

The number of sample paths to use in estimating the forecast distribution if simulated intervals are used.

...

Other arguments passed to methods

Value

A list of forecasts.

Examples

as_tsibble(USAccDeaths) %>%
  model(ets = ETS(log(value) ~ season("A"))) %>%
  forecast()
#> # A fable: 24 x 4 [1M]
#> # Key:     .model [1]
#>    .model    index             value  .mean
#>    <chr>     <mth>            <dist>  <dbl>
#>  1 ets    1979 Jan   t(N(9, 0.0011))  8296.
#>  2 ets    1979 Feb t(N(8.9, 0.0014))  7524.
#>  3 ets    1979 Mar   t(N(9, 0.0018))  8366.
#>  4 ets    1979 Apr t(N(9.1, 0.0022))  8622.
#>  5 ets    1979 May t(N(9.2, 0.0026))  9532.
#>  6 ets    1979 Jun  t(N(9.2, 0.003)) 10049.
#>  7 ets    1979 Jul t(N(9.3, 0.0035)) 10976.
#>  8 ets    1979 Aug  t(N(9.2, 0.004)) 10252.
#>  9 ets    1979 Sep t(N(9.1, 0.0045))  9169.
#> 10 ets    1979 Oct  t(N(9.2, 0.005))  9499.
#> # ℹ 14 more rows