momentum_ls: Lock-Step Variable Importance Analysis of Multivariate Time...

In distantia: Advanced Toolset for Efficient Time Series Dissimilarity Analysis

Lock-Step Variable Importance Analysis of Multivariate Time Series Lists

Description

Minimalistic but slightly faster version of momentum() to compute lock-step importance analysis in multivariate time series lists.

Usage

momentum_ls(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_dtw()

Examples

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

df <- momentum_ls(
  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.