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Dynamic Time Warping Variable Importance Analysis of Multivariate Time Series Lists

Source: R/momentum_dtw.R
momentum_dtw.Rd

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 series x.

  • y: name of the time series y.

  • psi: psi score of x and y.

  • 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"
  )]
#>       x    y    variable   importance               effect
#> 1  X132 X134           x   87.6066043 decreases similarity
#> 2  X132 X134           y   93.9587187 decreases similarity
#> 3  X132 X134       speed  -21.9171171 increases similarity
#> 4  X132 X134 temperature   72.8121621 decreases similarity
#> 5  X132 X134     heading  -38.0165137 increases similarity
#> 6  X132 X136           x   48.3845903 decreases similarity
#> 7  X132 X136           y   93.5214543 decreases similarity
#> 8  X132 X136       speed  -61.1729252 increases similarity
#> 9  X132 X136 temperature  356.8824838 decreases similarity
#> 10 X132 X136     heading -102.9830173 increases similarity
#> 11 X132 X153           x  427.7381576 decreases similarity
#> 12 X132 X153           y  156.1285451 decreases similarity
#> 13 X132 X153       speed  -40.9249630 increases similarity
#> 14 X132 X153 temperature  -14.2831545 increases similarity
#> 15 X132 X153     heading  -79.3532025 increases similarity
#> 16 X134 X136           x   61.3361468 decreases similarity
#> 17 X134 X136           y  108.9650664 decreases similarity
#> 18 X134 X136       speed  -59.2603918 increases similarity
#> 19 X134 X136 temperature  310.6812842 decreases similarity
#> 20 X134 X136     heading  -90.2797292 increases similarity
#> 21 X134 X153           x  592.0783167 decreases similarity
#> 22 X134 X153           y  116.4310429 decreases similarity
#> 23 X134 X153       speed  -52.4149093 increases similarity
#> 24 X134 X153 temperature    0.9936944 decreases similarity
#> 25 X134 X153     heading  -85.0271172 increases similarity
#> 26 X136 X153           x  507.6153648 decreases similarity
#> 27 X136 X153           y   56.6957442 decreases similarity
#> 28 X136 X153       speed  -65.4516103 increases similarity
#> 29 X136 X153 temperature  240.9053814 decreases similarity
#> 30 X136 X153     heading -116.2461929 increases similarity