View source: R/boilerplate-theta.R
| ts_auto_theta | R Documentation | 
This is a boilerplate function to create automatically the following:
recipe
model specification
workflow
calibration tibble and plot
ts_auto_theta(
  .data,
  .date_col,
  .value_col,
  .rsamp_obj,
  .prefix = "ts_theta",
  .bootstrap_final = FALSE
)
.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   | 
.bootstrap_final | 
 Not yet implemented.  | 
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.
A list
Steven P. Sanderson II, MPH
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()
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
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