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Auto NNETAR Workflowset Function

Source: R/wfs-nnetar-reg.R
ts_wfs_nnetar_reg.Rd

This function is used to quickly create a workflowsets object.

Usage

ts_wfs_nnetar_reg(
  .model_type = "nnetar",
  .recipe_list,
  .non_seasonal_ar = 0,
  .seasonal_ar = 0,
  .hidden_units = 5,
  .num_networks = 10,
  .penalty = 0.1,
  .epochs = 10
)

Arguments

.model_type

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

  • "nnetar"

.recipe_list

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

.non_seasonal_ar

The order of the non-seasonal auto-regressive (AR) terms. Often denoted "p" in pdq-notation.

.seasonal_ar

The order of the seasonal auto-regressive (SAR) terms. Often denoted "P" in PDQ-notation.

.hidden_units

An integer for the number of units in the hidden model.

.num_networks

Number of networks to fit with different random starting weights. These are then averaged when producing forecasts.

.penalty

A non-negative numeric value for the amount of weight decay.

.epochs

An integer for the number of training iterations.

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 uses the following engines:

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

  • "nnetar"

See also

https://workflowsets.tidymodels.org/

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

Other Auto Workflowsets: ts_wfs_arima_boost(), ts_wfs_auto_arima(), ts_wfs_ets_reg(), ts_wfs_lin_reg(), ts_wfs_mars(), 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_nnetar_reg("nnetar", rec_objs)
wf_sets
#> # A workflow set/tibble: 4 × 4
#>   wflow_id                        info             option    result    
#>   <chr>                           <list>           <list>    <list>    
#> 1 rec_base_nnetar_reg             <tibble [1 × 4]> <opts[0]> <list [0]>
#> 2 rec_date_nnetar_reg             <tibble [1 × 4]> <opts[0]> <list [0]>
#> 3 rec_date_fourier_nnetar_reg     <tibble [1 × 4]> <opts[0]> <list [0]>
#> 4 rec_date_fourier_nzv_nnetar_reg <tibble [1 × 4]> <opts[0]> <list [0]>