stamp_par | R Documentation |
Computes the best so far Matrix Profile and Profile Index for Univariate Time Series.
stamp_par( ..., window_size, exclusion_zone = getOption("tsmp.exclusion_zone", 1/2), verbose = getOption("tsmp.verbose", 2), s_size = Inf, n_workers = 2, weight = NULL ) stamp( ..., window_size, exclusion_zone = getOption("tsmp.exclusion_zone", 1/2), verbose = getOption("tsmp.verbose", 2), s_size = Inf, weight = NULL )
... |
a |
window_size |
an |
exclusion_zone |
a |
verbose |
an |
s_size |
a |
n_workers |
an |
weight |
a |
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.
Returns a MatrixProfile
object, a list
with the matrix profile mp
, profile index pi
left and right matrix profile lmp
, rmp
and profile index lpi
, rpi
, window size w
and
exclusion zone ez
.
stamp_par()
: Parallel version.
stamp()
: Single thread version.
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
Other matrix profile computations:
mstomp_par()
,
scrimp()
,
stomp_par()
,
tsmp()
,
valmod()
mp <- stamp(mp_toy_data$data[1:200, 1], window_size = 30, verbose = 0) #' # using threads mp <- stamp_par(mp_toy_data$data[1:200, 1], window_size = 30, verbose = 0) ref_data <- mp_toy_data$data[, 1] query_data <- mp_toy_data$data[, 2] # self similarity mp <- stamp(ref_data, window_size = 30, s_size = round(nrow(ref_data) * 0.1)) # join similarity mp <- stamp(ref_data, query_data, window_size = 30, s_size = round(nrow(query_data) * 0.1))
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