ml_models_wflw: A function to train simple univariate algorithms
In vidarsumo/sumots: Helper functions for Sumo Analytics

train_simple_models() is a function that allows you to train univariate time series models

ml_models_wflw(
  recipe_base,
  recipe_spline,
  recipe_lag,
  recipe_both,
  data = training(splits),
  use_models = c("glmnet", "mars", "svm_poly", "svm_rbf", "knn", "rf", "xgboost",
    "cubist", "nnet", "nnetar", "arima_boost", "prophet_boost"),
  skip_models = NULL
)

`recipe_base`	Recipe for the modeltime algorithms and NNETAR
`recipe_spline`	Recipe with natural spliens
`recipe_lag`	Recipe with lags
`recipe_both`	Recipe with splines and lags
`data`	Data to use
`use_models`	Models to use
`skip_models`	Character vector of models to skip, defaults to NULL