distantia_dtw {distantia} | R Documentation |
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 sequencesx
andy
.
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"
)
}
[Package distantia version 2.0.2 Index]