distantia_ls: Lock-Step Dissimilarity Analysis of Time Series Lists

In distantia: Advanced Toolset for Efficient Time Series Dissimilarity Analysis

Lock-Step Dissimilarity Analysis of Time Series Lists

Description

Minimalistic but slightly faster version of distantia() to compute lock-step dissimilarity scores.

Usage

distantia_ls(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:

• 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(), distantia_dtw_plot()

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
    )
}

#lock-step dissimilarity analysis
df_ls <- distantia_ls(
  tsl = tsl,
  distance = "euclidean"
)

df_ls

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