distantia_dtw: Dynamic Time Warping Dissimilarity Analysis of Time Series...

In distantia: Advanced Toolset for Efficient Time Series Dissimilarity Analysis

Dynamic Time Warping Dissimilarity Analysis of Time Series Lists

Description

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")]

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

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

}

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