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 model_mean
generate(x, new_data, bootstrap = FALSE, ...)
A fitted model.
A tsibble containing the time points and exogenous regressors to produce forecasts for.
If TRUE
, then forecast distributions are computed using simulation with resampled errors.
Other arguments passed to methods
library(tsibbledata)
vic_elec %>%
model(avg = MEAN(Demand)) %>%
generate()
#> # A tsibble: 4 x 4 [30m] <Australia/Melbourne>
#> # Key: .model, .rep [1]
#> .model .rep Time .sim
#> <chr> <chr> <dttm> <dbl>
#> 1 avg 1 2015-01-01 00:00:00 5425.
#> 2 avg 1 2015-01-01 00:30:00 4485.
#> 3 avg 1 2015-01-01 01:00:00 4731.
#> 4 avg 1 2015-01-01 01:30:00 2698.