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

Source: R/RcppExports.R
importance_dynamic_time_warping_robust_cpp.Rd

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.

Usage

importance_dynamic_time_warping_robust_cpp(
  x,
  y,
  distance = "euclidean",
  diagonal = TRUE,
  weighted = TRUE,
  ignore_blocks = FALSE
)

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). If TRUE, diagonal is set to TRUE, and diagonal cost is weighted by a factor of 1.414214. Default: TRUE.

ignore_blocks

(optional, logical). If TRUE, blocks of consecutive path coordinates are trimmed to avoid inflating the psi distance. Default: FALSE.

Value

data frame

See also

Other Rcpp_importance: importance_dynamic_time_warping_legacy_cpp(), importance_lock_step_cpp()

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)
#> [1] FALSE

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

df
#>   variable     psi psi_only_with psi_without psi_difference    importance
#> 1        a 6.90895      6.968817    6.969328  -0.0005109117  -0.007394926
#> 2        b 6.90895      7.283144    6.930810   0.3523345221   5.099682515
#> 3        c 6.90895      7.214624    6.815044   0.3995803049   5.783516989
#> 4        d 6.90895      6.758557    6.704001   0.0545567459   0.789653200
#> 5        e 6.90895      6.036200    7.133427  -1.0972265521 -15.881234203