Clustering time series using dynamic time warping

Clusters a time series using dtwclust::tsclust. The number of clusters is tunable with k. Additional parameters can be set using options.

Adds a single column to new data (per input column), with integers 1-k identifying the cluster.

step_dtw(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  k = 4,
  dtwclust = NULL,
  options = list(),
  skip = FALSE,
  id = recipes::rand_id("dtw")
)

Arguments

recipe	A recipe object. The step will be added to the sequence of operations for this recipe.
...	One or more selector functions to choose which variables are affected by the step. See selections() for more details. For the tidy method, these are not currently used.
role	For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new columns created from the original variables will be used as predictors in a model.
trained	A logical to indicate if the quantities for preprocessing have been estimated.
k	The number of clusters, tunable.
dtwclust	A list of TClusters objects for each list of time series passed to the step, created once the step has been trained.
options	A list of options for splines::bs() which should not include x, degree, or df.
skip	A logical. Should the step be skipped when the recipe is baked by bake.recipe()? While all operations are baked when prep.recipe() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations
id	A character string that is unique to this step to identify it.