Boilerplate Workflow

This is a boilerplate function to create automatically the following:

• recipe

• model specification

• workflow

• calibration tibble and plot

Usage

ts_auto_theta(
  .data,
  .date_col,
  .value_col,
  .rsamp_obj,
  .prefix = "ts_theta",
  .bootstrap_final = FALSE
)

Arguments

.data

The data being passed to the function. The time-series object.

.date_col

The column that holds the datetime.

.value_col

The column that has the value

.rsamp_obj

The splits object

.prefix

Default is ts_theta

.bootstrap_final

Not yet implemented.

Value

A list

Details

This uses the forecast::thetaf() for the parsnip engine. This model does not use exogenous regressors, so only a univariate model of: value ~ date will be used from the .date_col and .value_col that you provide.

See also

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

https://pkg.robjhyndman.com/forecast/reference/thetaf.html

Other Boiler_Plate: ts_auto_arima(), ts_auto_arima_xgboost(), ts_auto_croston(), ts_auto_exp_smoothing(), ts_auto_glmnet(), ts_auto_lm(), ts_auto_mars(), ts_auto_nnetar(), ts_auto_prophet_boost(), ts_auto_prophet_reg(), ts_auto_smooth_es(), ts_auto_svm_poly(), ts_auto_svm_rbf(), ts_auto_xgboost()

Other exp_smoothing: ts_auto_croston(), ts_auto_exp_smoothing(), ts_auto_smooth_es()

Author

Steven P. Sanderson II, MPH

Examples

# \donttest{
library(dplyr)
library(timetk)
library(modeltime)

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

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

ts_theta <- ts_auto_theta(
  .data = data,
  .date_col = date_col,
  .value_col = value,
  .rsamp_obj = splits
)

ts_theta$recipe_info
#> $recipe_call
#> recipe(.data = data, .date_col = date_col, .value_col = value, 
#>     .rsamp_obj = splits)
#> 
#> $recipe_syntax
#> [1] "ts_theta_recipe <-"                                                                     
#> [2] "\n  recipe(.data = data, .date_col = date_col, .value_col = value, .rsamp_obj = splits)"
#> 
#> $rec_obj
#> 
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> outcome:   1
#> predictor: 1
#> 
# }