Forecast a model from the fable package

Produces forecasts from a trained model.

# S3 method for TSLM
forecast(
  object,
  new_data,
  specials = NULL,
  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()).
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(USAccDeaths) %>%
  model(lm = TSLM(log(value) ~ trend() + season())) %>%
  forecast()
#> # A fable: 24 x 4 [1M]
#> # Key:     .model [1]
#>    .model    index            value .mean
#>    <chr>     <mth>           <dist> <dbl>
#>  1 lm     1979 Jan t(N(8.9, 0.003)) 7620.
#>  2 lm     1979 Feb t(N(8.8, 0.003)) 6899.
#>  3 lm     1979 Mar t(N(8.9, 0.003)) 7639.
#>  4 lm     1979 Apr   t(N(9, 0.003)) 7841.
#>  5 lm     1979 May t(N(9.1, 0.003)) 8645.
#>  6 lm     1979 Jun t(N(9.1, 0.003)) 9087.
#>  7 lm     1979 Jul t(N(9.2, 0.003)) 9908.
#>  8 lm     1979 Aug t(N(9.1, 0.003)) 9237.
#>  9 lm     1979 Sep   t(N(9, 0.003)) 8237.
#> 10 lm     1979 Oct   t(N(9, 0.003)) 8516.
#> # … with 14 more rows