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 class 'model_mean'
generate(x, new_data, bootstrap = FALSE, ...)
library(tsibbledata)
vic_elec %>%
model(avg = MEAN(Demand)) %>%
generate()
#> # A tsibble: 4 x 4 [30m] <Australia/Melbourne>
#> # Key: .model, .rep [1]
#> .model Time .rep .sim
#> <chr> <dttm> <chr> <dbl>
#> 1 avg 2015-01-01 00:00:00 1 4397.
#> 2 avg 2015-01-01 00:30:00 1 4478.
#> 3 avg 2015-01-01 01:00:00 1 4591.
#> 4 avg 2015-01-01 01:30:00 1 4514.