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, bootstrap = FALSE, bootstrap_block_size = 1, ... )
| 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. |
| bootstrap | If TRUE, then forecast distributions are computed using simulation with resampled errors. |
| bootstrap_block_size | The bootstrap block size specifies the number of contiguous residuals to be taken in each bootstrap sample. |
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..
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) #> # A tsibble: 360 x 4 [1M] #> # Key: .model, .rep [5] #> .model index .rep .sim #> <chr> <mth> <chr> <dbl> #> 1 lm 1974 Jan 1 2285. #> 2 lm 1974 Feb 1 1802. #> 3 lm 1974 Mar 1 2133. #> 4 lm 1974 Apr 1 1763. #> 5 lm 1974 May 1 1413. #> 6 lm 1974 Jun 1 1106. #> 7 lm 1974 Jul 1 1209. #> 8 lm 1974 Aug 1 1075. #> 9 lm 1974 Sep 1 1060. #> 10 lm 1974 Oct 1 1529. #> # … with 350 more rows