Lock-Step Dissimilarity Analysis of Time Series Lists

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

Usage

distantia_lock_step(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_plot(), distantia_time_warp()

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_lock_step(
  tsl = tsl,
  distance = "euclidean"
)

#focus on the important details
df_ls[, c("x", "y", "psi")]
#>         x      y      psi
#> 2 Germany Sweden 0.857670
#> 1 Germany  Spain 1.306133
#> 3   Spain Sweden 1.470850