importance_ls_cpp: (C++) Contribution of Individual Variables to the...

In distantia: Advanced Toolset for Efficient Time Series Dissimilarity Analysis

(C++) Contribution of Individual Variables to the Dissimilarity Between Two Aligned Time Series

Description

Computes the contribution of individual variables to the similarity/dissimilarity between two aligned multivariate time series. This function generates a data frame with the following columns:

• variable: name of the individual variable for which the importance is being computed, from the column names of the arguments x and y.

• psi: global dissimilarity score psi of the two time series.

• psi_only_with: dissimilarity between x and y computed from the given variable alone.

• psi_without: dissimilarity between x and y computed from all other variables.

• psi_difference: difference between psi_only_with and psi_without.

• importance: contribution of the variable to the similarity/dissimilarity between x and y, computed as (psi_difference * 100) / psi_all. Positive scores represent contribution to dissimilarity, while negative scores represent contribution to similarity.

Usage

importance_ls_cpp(x, y, distance = "euclidean")

Arguments

x	(required, numeric matrix) multivariate time series.
y	(required, numeric matrix) multivariate time series with the same number of columns and rows as 'x'.
distance	(optional, character string) distance name from the "names" column of the dataset distances (see distances$name). Default: "euclidean".

Value

data frame

See Also

Other Rcpp_importance: importance_dtw_cpp(), importance_dtw_legacy_cpp()

Examples

#simulate two regular time series
x <- zoo_simulate(
  seed = 1,
  irregular = FALSE
  )

y <- zoo_simulate(
  seed = 2,
  irregular = FALSE
  )

#same number of rows
nrow(x) == nrow(y)

#compute importance
df <- importance_ls_cpp(
  x = x,
  y = y,
  distance = "euclidean"
)

df

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