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Auto Arima (Forecast auto_arima) Workflowset Function

Source: R/wfs-arima-reg.R
ts_wfs_auto_arima.Rd

This function is used to quickly create a workflowsets object.

Usage

ts_wfs_auto_arima(.model_type = "auto_arima", .recipe_list)

Arguments

.model_type

This is where you will set your engine. It uses modeltime::arima_reg() under the hood and can take one of the following:

  • "auto_arima"

.recipe_list

You must supply a list of recipes. list(rec_1, rec_2, ...)

Value

Returns a workflowsets object.

Details

This function expects to take in the recipes that you want to use in the modeling process. This is an automated workflow process. There are sensible defaults set for the model specification, but if you choose you can set them yourself if you have a good understanding of what they should be. The mode is set to "regression".

This only uses the option set_engine("auto_arima") and therefore the .model_type is not needed. The parameter is kept because it is possible in the future that this could change, and it keeps with the framework of how other functions are written.

modeltime::arima_reg() arima_reg() is a way to generate a specification of an ARIMA model before fitting and allows the model to be created using different packages. Currently the only package is forecast.

See also

https://workflowsets.tidymodels.org/

https://business-science.github.io/modeltime/reference/arima_reg.html

Other Auto Workflowsets: ts_wfs_arima_boost(), ts_wfs_ets_reg(), ts_wfs_lin_reg(), ts_wfs_mars(), ts_wfs_nnetar_reg(), ts_wfs_prophet_reg(), ts_wfs_svm_poly(), ts_wfs_svm_rbf(), ts_wfs_xgboost()

Author

Steven P. Sanderson II, MPH

Examples

suppressPackageStartupMessages(library(modeltime))
suppressPackageStartupMessages(library(timetk))
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(rsample))

data <- AirPassengers %>%
  ts_to_tbl() %>%
  select(-index)

splits <- time_series_split(
   data
  , date_col
  , assess = 12
  , skip = 3
  , cumulative = TRUE
)

rec_objs <- ts_auto_recipe(
 .data = training(splits)
 , .date_col = date_col
 , .pred_col = value
)

wf_sets <- ts_wfs_auto_arima("auto_arima", rec_objs)
wf_sets
#> # A workflow set/tibble: 4 × 4
#>   wflow_id                       info             option    result    
#>   <chr>                          <list>           <list>    <list>    
#> 1 rec_base_arima_reg             <tibble [1 × 4]> <opts[0]> <list [0]>
#> 2 rec_date_arima_reg             <tibble [1 × 4]> <opts[0]> <list [0]>
#> 3 rec_date_fourier_arima_reg     <tibble [1 × 4]> <opts[0]> <list [0]>
#> 4 rec_date_fourier_nzv_arima_reg <tibble [1 × 4]> <opts[0]> <list [0]>