momentum_dtw: Dynamic Time Warping Variable Importance Analysis of...

In distantia: Advanced Toolset for Efficient Time Series Dissimilarity Analysis

Dynamic Time Warping Variable Importance Analysis of Multivariate Time Series Lists

Description

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

Usage

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".

Value

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".

See Also

Other momentum: momentum(), momentum_ls()

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"
  )]

distantia documentation built on April 4, 2025, 5:42 a.m.