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Contrast Profile Computation

Source: R/contrast.R
contrast.Rd

Computes the contrast profile of two (classes of) time series.

Usage

contrast(
  negative_data,
  positive_data,
  window_size,
  positive_matrix = NULL,
  exclusion_zone = 0.5,
  distance = c("euclidean", "pearson"),
  n_workers = 1L,
  progress = TRUE
)

Arguments

negative_data

Required. Any 1-dimension series of numbers (matrix, vector, ts etc.) where the pattern is not present

positive_data

Required. Any 1-dimension series of numbers (matrix, vector, ts etc.) where the pattern is present

window_size

Required. An integer defining the rolling window size.

positive_matrix

Optional. A precomputed self-similar matrix profile of the positive data.

exclusion_zone

A numeric. Defines the size of the area around the rolling window that will be ignored to avoid trivial matches. Default is 0.5, i.e., half of the window_size.

distance

A string. Currently accepts euclidean and pearson. Defaults to euclidean.

n_workers

An integer. The number of threads using for computing. Defaults to 1. For parallel execution across processes, prefer a PSOCK cluster and use n_workers = 1 within each worker. See matrixprofiler for fork limitations.

progress

A logical. If TRUE (the default) will show a progress bar. Useful for long computations.

Value

Returns a list with the contrast_profile, plato, plato_nn, plato_idx, plato_nn_idx, w, ez, euclidean values

Details

Constrast Profile

This algorithm returns the contrast profile of two time series, which shows the position of patters that are similar in the positive data, but at the same time very dissimilar in the negative data. In other words, this means that such a pattern represents well positive data and may be taken as a "signature" of that class. More information can be found in the references.

References

  • R. Mercer, S. Alaee, A. Abdoli, S. Singh, A. Murillo and E. Keogh, "Matrix Profile XXIII: Contrast Profile: A Novel Time Series Primitive that Allows Real World Classification," 2021 IEEE International Conference on Data Mining (ICDM), 2021, pp. 1240-1245, doi: 10.1109/ICDM51629.2021.00151.

Website: http://www.cs.ucr.edu/~eamonn/MatrixProfile.html

Examples

cp <- contrast(motifs_discords_small, rev(motifs_discords_small), 50)
#> 
Computing: [========================================] 100% (done)