momentum: Contribution of Individual Variables to Time Series...

In distantia: Advanced Toolset for Efficient Time Series Dissimilarity Analysis

Contribution of Individual Variables to Time Series Dissimilarity

Description

This function measures the contribution of individual variables to the dissimilarity between pairs of time series to help answer the question what makes two time series more or less similar?

Three key values are required to assess individual variable contributions:

• psi: dissimilarity when all variables are considered.

• psi_only_with: dissimilarity when using only the target variable.

• psi_without: dissimilarity when removing the target variable.

The values psi_only_with and psi_without can be computed in two different ways defined by the argument robust.

• robust = FALSE: This method replicates the importance algorithm released with the first version of the package, and it is only recommended when the goal to compare new results with previous studies. It normalizes psi_only_with and psi_without using the least cost path obtained from the individual variable. As different variables may have different least cost paths for the same time series, normalization values may change from variable to variable, making individual importance scores harder to compare.

• robust = TRUE (default, recommended): This a novel version of the importance algorithm that yields more stable and comparable solutions. It uses the least cost path of the complete time series to normalize psi_only_with and psi_without, making importance scores of separate variables fully comparable.

The individual importance score of each variable (column "importance" in the output data frame) is based on different expressions depending on the robust argument, even when lock_step = TRUE:

• robust = FALSE: Importance is computed as ((psi - psi_without) * 100)/psi and interpreted as "change in similarity when a variable is removed".

• robust = TRUE: Importance is computed as ((psi_only_with - psi_without) * 100)/psi and interpreted as "relative dissimilarity induced by the variable expressed as a percentage".

In either case, positive values indicate that the variable contributes to dissimilarity, while negative values indicate a net contribution to similarity.

This function allows computing dissimilarity between pairs of time series using different combinations of arguments at once. For example, when the argument distance is set to c("euclidean", "manhattan"), the output data frame will show two dissimilarity scores for each pair of time series, one based on euclidean distances, and another based on manhattan distances. The same happens for most other parameters.

This function supports a parallelization setup via future::plan(), and progress bars provided by the package progressr.

Usage

momentum(
  tsl = NULL,
  distance = "euclidean",
  diagonal = TRUE,
  bandwidth = 1,
  lock_step = FALSE,
  robust = TRUE
)

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".
diagonal	(optional, logical vector). If TRUE, diagonals are included in the dynamic time warping computation. Default: TRUE
bandwidth	(optional, numeric) Proportion of space at each side of the cost matrix diagonal (aka Sakoe-Chiba band) defining a valid region for dynamic time warping, used to control the flexibility of the warping path. This method prevents degenerate alignments due to differences in magnitude between time series when the data is not properly scaled. If 1 (default), DTW is unconstrained. If 0, DTW is fully constrained and the warping path follows the matrix diagonal. Recommended values may vary depending on the nature of the data. Ignored if lock_step = TRUE. Default: 1.
lock_step	(optional, logical vector) If TRUE, time series captured at the same times are compared sample wise (with no dynamic time warping). Requires time series in argument tsl to be fully aligned, or it will return an error. Default: FALSE.
robust	(required, logical). If TRUE (default), importance scores are computed using the least cost path of the complete time series as reference. Setting it to FALSE allows to replicate importance scores of the previous versions of this package. This option is irrelevant when lock_step = TRUE. Default: TRUE

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".

• psi_only_with: psi score of the variable.

• psi_without: psi score without the variable.

• psi_difference: difference between psi_only_with and psi_without.

• distance: name of the distance metric.

• diagonal: value of the argument diagonal.

• lock_step: value of the argument lock_step.

• robust: value of the argument robust.

See Also

Other momentum: momentum_dtw(), momentum_ls()

Examples

#progress bar
# progressr::handlers(global = TRUE)

tsl <- tsl_initialize(
  x = distantia::albatross,
  name_column = "name",
  time_column = "time"
) |>
  tsl_transform(
    f = f_scale_global
  )

df <- momentum(
  tsl = tsl,
  lock_step = TRUE #to speed-up example
  )

#focus on important columns
df[, c(
  "x",
  "y",
  "variable",
  "importance",
  "effect"
  )]

distantia documentation built on April 4, 2025, 5:42 a.m.