Produces forecasts from a trained model.
Usage
# S3 method for LOGISTIC
forecast(
object,
new_data,
specials = NULL,
simulate = 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, then forecast distributions are computed using simulation from a Bernoulli model.- times
The number of sample paths to use in estimating the forecast distribution when
simulate = TRUE.- ...
Other arguments passed to methods
Examples
melb_rain |>
model(logistic = LOGISTIC(Wet ~ fourier(K = 5, period = "year"))) |>
forecast(h = "2 years")
#> # A fable: 730 x 4 [1D]
#> # Key: .model [1]
#> .model Date Wet .mean
#> <chr> <date> <dist> <dbl>
#> 1 logistic 2011-11-01 B(1, 0.41) 0.413
#> 2 logistic 2011-11-02 B(1, 0.41) 0.412
#> 3 logistic 2011-11-03 B(1, 0.41) 0.411
#> 4 logistic 2011-11-04 B(1, 0.41) 0.410
#> 5 logistic 2011-11-05 B(1, 0.41) 0.409
#> 6 logistic 2011-11-06 B(1, 0.41) 0.408
#> 7 logistic 2011-11-07 B(1, 0.41) 0.407
#> 8 logistic 2011-11-08 B(1, 0.41) 0.405
#> 9 logistic 2011-11-09 B(1, 0.4) 0.404
#> 10 logistic 2011-11-10 B(1, 0.4) 0.402
#> # ℹ 720 more rows