Forecast a model from the fable package

Produces forecasts from a trained model.

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

Arguments

object	The time series model used to produce the forecasts
new_data	A tsibble containing future information used to forecast.
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 when bootstrap = TRUE.
...	Additional arguments for forecast model methods.

Value

A list of forecasts.

Examples

as_tsibble(Nile) %>%
  model(NAIVE(value)) %>%
  forecast()
#> # A fable: 2 x 4 [1Y]
#> # Key:     .model [1]
#>   .model       index         value .mean
#>   <chr>        <dbl>        <dist> <dbl>
#> 1 NAIVE(value)  1971 N(740, 27998)   740
#> 2 NAIVE(value)  1972 N(740, 55995)   740

library(tsibbledata)
aus_production %>%
  model(snaive = SNAIVE(Beer ~ lag("year"))) %>%
  forecast()
#> # A fable: 8 x 4 [1Q]
#> # Key:     .model [1]
#>   .model Quarter        Beer .mean
#>   <chr>    <qtr>      <dist> <dbl>
#> 1 snaive 2010 Q3 N(419, 373)   419
#> 2 snaive 2010 Q4 N(488, 373)   488
#> 3 snaive 2011 Q1 N(414, 373)   414
#> 4 snaive 2011 Q2 N(374, 373)   374
#> 5 snaive 2011 Q3 N(419, 747)   419
#> 6 snaive 2011 Q4 N(488, 747)   488
#> 7 snaive 2012 Q1 N(414, 747)   414
#> 8 snaive 2012 Q2 N(374, 747)   374