Forecast of a workflow set on multiple time series

generates forecasts of a workflow set object over multiple time series.

modeltime_wfs_multiforecast(models_table, .h = NULL, .prop = NULL)

Arguments

models_table	a tibble that comes from the output of the modeltime_wfs_multifit(), modeltime_wfs_multirefit(), modeltime_wfs_multibestmodel() functions. For the modeltime_wfs_multifit() function, the 'table_time' object must be selected from the output.
.h	prediction horizon of the modeltime_forecast() function of the 'modeltime' package.
.prop	decimal number, time series split partition ratio. If ".h" is specified, this function predicts on the testing partition.

Value

a tibble, corresponds to the same tibble supplied in the 'models_table' parameter but with an additional column called 'nested_forecast' where the nested previews of the workflows on all the time series are stored.

Examples

library(dplyr)
library(earth)

df <- sknifedatar::emae_series

datex <- '2020-02-01'
df_emae <- df %>%
  dplyr::filter(date <= datex) %>% 
  tidyr::nest(nested_column=-sector) %>% head(2)

receta_base <- recipes::recipe(value ~ ., data = df %>% select(-sector))

mars <- parsnip::mars(mode = 'regression') %>% parsnip::set_engine('earth')

wfsets <- workflowsets::workflow_set(
  preproc = list(
    R_date = receta_base),
  models  = list(M_mars = mars),
  cross   = TRUE)

wfsets_fit <- sknifedatar::modeltime_wfs_multifit(.wfs = wfsets,
                                                  .prop = 0.8, 
                                                  serie = df_emae)
#> ✓ Workflow training finished OK.
#> ✓ Workflow training finished OK.
#> 
#> ── 1 models fitted ♥ ───────────────────────────────────────────────────────────
#> 
#> ── 0 models deleted x ──
#> 

sknifedatar::modeltime_wfs_multiforecast(wfsets_fit$table_time,
                                         .prop=0.8)
#> Joining, by = "models_freq"
#> Joining, by = "models_freq"
#> # A tibble: 2 x 6
#>   sector  nested_column  R_date_M_mars nested_model  calibration nested_forecast
#>   <chr>   <list>         <list>        <list>        <list>      <list>         
#> 1 Comerc… <tibble [194 … <workflow>    <model_time … <model_tim… <tibble [233 ×…
#> 2 Ensena… <tibble [194 … <workflow>    <model_time … <model_tim… <tibble [233 ×…