healthyR.ts (version 0.3.1)

ts_wfs_ets_reg: Auto ETS Workflowset Function

Description

This function is used to quickly create a workflowsets object.

Usage

ts_wfs_ets_reg(
  .model_type = "all_engines",
  .recipe_list,
  .seasonal_period = "auto",
  .error = "auto",
  .trend = "auto",
  .season = "auto",
  .damping = "auto",
  .smooth_level = 0.1,
  .smooth_trend = 0.1,
  .smooth_seasonal = 0.1
)

Value

Returns a workflowsets object.

Arguments

.model_type

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

• "ets"

• "croston"

• "theta"

• "smooth_es"

• "all_engines" - This will make a model spec for all available engines.

.recipe_list

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

.seasonal_period

A seasonal frequency. Uses "auto" by default. A character phrase of "auto" or time-based phrase of "2 weeks" can be used if a date or date-time variable is provided. See Fit Details below.

.error

The form of the error term: "auto", "additive", or "multiplicative". If the error is multiplicative, the data must be non-negative.

.trend

The form of the trend term: "auto", "additive", "multiplicative" or0 "none".

.season

The form of the seasonal term: "auto", "additive", "multiplicative" or "none".

.damping

Apply damping to a trend: "auto", "damped", or "none".

.smooth_level

This is often called the "alpha" parameter used as the base level smoothing factor for exponential smoothing models.

.smooth_trend

This is often called the "beta" parameter used as the trend smoothing factor for exponential smoothing models.

.smooth_seasonal

This is often called the "gamma" parameter used as the seasonal smoothing factor for exponential smoothing models.

Author

Steven P. Sanderson II, MPH

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::exp_smoothing() exp_smoothing() is a way to generate a specification of an Exponential Smoothing model before fitting and allows the model to be created using different packages. Currently the only package is forecast. Several algorithms are implemented:

• "ets"

• "croston"

• "theta"

• "smooth_es

See Also

https://workflowsets.tidymodels.org/

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

Other Auto Workflowsets: ts_wfs_arima_boost(), ts_wfs_auto_arima(), 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()

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_ets_reg("all_engines", rec_objs)
wf_sets