importance_dtw_cpp function

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

Computes the contribution of individual variables to the similarity/dissimilarity between two irregular multivariate time series. In opposition to the legacy version, importance computation is performed taking the least-cost path of the whole sequence as reference. This operation makes the importance scores of individual variables fully comparable. 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.

importance_dtw_cpp(
  x,
  y,
  distance = "euclidean",
  diagonal = TRUE,
  weighted = TRUE,
  ignore_blocks = FALSE,
  bandwidth = 1
)

Arguments

• x: (required, numeric matrix) multivariate time series.
• y: (required, numeric matrix) multivariate time series with the same number of columns as 'x'.
• distance: (optional, character string) distance name from the "names" column of the dataset distances (see distances$name). Default: "euclidean".
• diagonal: (optional, logical). If TRUE, diagonals are included in the computation of the cost matrix. Default: TRUE.
• weighted: (optional, logical). Only relevant when diagonal is TRUE. When TRUE, diagonal cost is weighted by y factor of 1.414214 (square root of 2). Default: TRUE.
• ignore_blocks: (optional, logical). If TRUE, blocks of consecutive path coordinates are trimmed to avoid inflating the psi distance. Default: FALSE.
• bandwidth: (required, numeric) Size of the Sakoe-Chiba band at both sides of the diagonal used to constrain the least cost path. Expressed as a fraction of the number of matrix rows and columns. Unrestricted by default. Default: 1

Returns

data frame

Examples

#simulate two regular time series
x <- zoo_simulate(
  seed = 1,
  rows = 100
  )

y <- zoo_simulate(
  seed = 2,
  rows = 150
  )

#different number of rows
#this is not a requirement though!
nrow(x) == nrow(y)

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

df

See Also

Other Rcpp_importance: importance_dtw_legacy_cpp(), importance_ls_cpp()