momentum_dtw function

Dynamic Time Warping Variable Importance Analysis of Multivariate Time Series Lists

Minimalistic but slightly faster version of momentum() to compute dynamic time warping importance analysis with the "robust" setup in multivariate time series lists.

momentum_dtw(tsl = NULL, distance = "euclidean")

Arguments

• tsl: (required, time series list) list of zoo time series. Default: NULL
• distance: (optional, character vector) name or abbreviation of the distance method. Valid values are in the columns "names" and "abbreviation" of the dataset distances . Default: "euclidean".

Returns

data frame:

• x: name of the time series x.
• y: name of the time series y.
• psi: psi score of x and y.
• variable: name of the individual variable.
• importance: importance score of the variable.
• effect: interpretation of the "importance" column, with the values "increases similarity" and "decreases similarity".

Examples

tsl <- tsl_initialize(
  x = distantia::albatross,
  name_column = "name",
  time_column = "time"
) |>
  tsl_transform(
    f = f_scale_global
  )

df <- momentum_dtw(
  tsl = tsl,
  distance = "euclidean"
  )

#focus on important columns
df[, c(
  "x",
  "y",
  "variable",
  "importance",
  "effect"
  )]

See Also

Other momentum: momentum(), momentum_ls()