Anytime univariate STAMP algorithm

Source: R/stamp.R

Computes the best so far Matrix Profile and Profile Index for Univariate Time Series.

stamp(
  data,
  window_size,
  query = NULL,
  exclusion_zone = 0.5,
  n_workers = 1,
  progress = TRUE
)

Arguments

data

Required. Any 1-dimension series of numbers (matrix, vector, ts etc.) (See details).
window_size

Required. An integer defining the rolling window size.
query

Optional. Another 1-dimension series of numbers for an AB-join similarity. Default is NULL (See details).
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.
n_workers

An integer. The number of threads using for computing. Defaults to 1.
progress

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

Details

The Matrix Profile, has the potential to revolutionize time series data mining because of its generality, versatility, simplicity and scalability. In particular it has implications for time series motif discovery, time series joins, shapelet discovery (classification), density estimation, semantic segmentation, visualization, rule discovery, clustering etc. The anytime STAMP computes the Matrix Profile and Profile Index in such manner that it can be stopped before its complete calculation and return the best so far results allowing ultra-fast approximate solutions. verbose changes how much information is printed by this function; 0 means nothing, 1 means text, 2 adds the progress bar, 3 adds the finish sound. exclusion_zone is used to avoid trivial matches; if a query data is provided (join similarity), this parameter is ignored.

Functions

  • stamp: Single thread version.

References

  • Yeh CCM, Zhu Y, Ulanova L, Begum N, Ding Y, Dau HA, et al. Matrix profile I: All pairs similarity joins for time series: A unifying view that includes motifs, discords and shapelets. Proc - IEEE Int Conf Data Mining, ICDM. 2017;1317-22.

  • Zhu Y, Imamura m, Nikovski D, Keogh E. Matrix Profile VII: Time Series Chains: A New Primitive for Time Series Data Mining. Knowl Inf Syst. 2018 Jun 2;1-27.

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

See also

Other matrix profile computations: mpx(), scrimp(), stomp()

Examples

# \donttest{
mp <- stamp(runif(200), window_size = 30)

#> <simpleError in stamp_rcpp(data, data, window_size, ez, as.logical(progress)): object '_matrixprofiler_stamp_rcpp' not found>
# }