Use a model's fitted distribution to simulate additional data with similar
behaviour to the response. This is a tidy implementation of
\link[stats]{simulate}.
# S3 method for mdl_df generate(x, new_data = NULL, h = NULL, times = 1, seed = NULL, ...) # S3 method for mdl_ts generate(x, new_data = NULL, h = NULL, times = 1, seed = NULL, ...)
| x | A mable. |
|---|---|
| new_data | The data to be generated (time index and exogenous regressors) |
| h | The simulation horizon (can be used instead of |
| times | The number of replications. |
| seed | The seed for the random generation from distributions. |
| ... | Additional arguments for individual simulation methods. |
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 for the simulated paths..
if (requireNamespace("fable", quietly = TRUE)) { library(fable) library(dplyr) UKLungDeaths <- as_tsibble(cbind(mdeaths, fdeaths), pivot_longer = FALSE) UKLungDeaths %>% model(lm = TSLM(mdeaths ~ fourier("year", K = 4) + fdeaths)) %>% generate(UKLungDeaths, times = 5) }#> Warning: `time_unit()` is deprecated as of tsibble 0.9.0. #> Please use `default_time_units()` instead. #> This warning is displayed once per session. #> Call `lifecycle::last_warnings()` to see where this warning was generated.#> Error: `vars` must be a character vector