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Dynamic Time Warping Dissimilarity Analysis of Time Series Lists

Source: R/distantia_dtw.R
distantia_dtw.Rd

Minimalistic but slightly faster version of distantia() to compute dynamic time warping dissimilarity scores using diagonal least cost paths.

Usage

distantia_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 with columns:

  • x: time series name.

  • y: time series name.

  • distance: name of the distance metric.

  • psi: psi dissimilarity of the sequences x and y.

See also

Other distantia: distantia(), distantia_dtw_plot(), distantia_ls()

Examples


#load fagus_dynamics as tsl
#global centering and scaling
tsl <- tsl_initialize(
  x = fagus_dynamics,
  name_column = "name",
  time_column = "time"
) |>
  tsl_transform(
    f = f_scale_global
  )

if(interactive()){
  tsl_plot(
    tsl = tsl,
    guide_columns = 3
    )
}

#dynamic time warping dissimilarity analysis
df_dtw <- distantia_dtw(
  tsl = tsl,
  distance = "euclidean"
)

df_dtw[, c("x", "y", "psi")]
#>         x      y       psi
#> 2 Germany Sweden 0.8571217
#> 1 Germany  Spain 1.3429956
#> 3   Spain Sweden 1.4803954

#visualize dynamic time warping
if(interactive()){

  distantia_dtw_plot(
    tsl = tsl[c("Spain", "Sweden")],
    distance = "euclidean"
  )

}