momentum_dtw {distantia} | R Documentation |
Dynamic Time Warping Variable Importance Analysis of Multivariate Time Series Lists
Description
Minimalistic but slightly faster version of momentum()
to compute dynamic time warping importance analysis with the "robust" setup in multivariate time series lists.
Usage
momentum_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:
-
x
: name of the time seriesx
. -
y
: name of the time seriesy
. -
psi
: psi score ofx
andy
. -
variable
: name of the individual variable. -
importance
: importance score of the variable. -
effect
: interpretation of the "importance" column, with the values "increases similarity" and "decreases similarity".
See Also
Other momentum:
momentum()
,
momentum_ls()
Examples
tsl <- tsl_initialize(
x = distantia::albatross,
name_column = "name",
time_column = "time"
) |>
tsl_transform(
f = f_scale_global
)
df <- momentum_dtw(
tsl = tsl,
distance = "euclidean"
)
#focus on important columns
df[, c(
"x",
"y",
"variable",
"importance",
"effect"
)]