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Lock-Step Dissimilarity Analysis of Time Series Lists

Source: R/distantia_ls.R
distantia_ls.Rd

A minimalistic but slightly faster version of distantia() that computes 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.

  • lock_step: always TRUE; flags this result as lock-step.

  • 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
#>         x      y  distance lock_step      psi
#> 2 Germany Sweden euclidean      TRUE 0.857670
#> 1 Germany  Spain euclidean      TRUE 1.306133
#> 3   Spain Sweden euclidean      TRUE 1.470850