fluss: Fast Low-cost Unipotent Semantic Segmentation (FLUSS)

In franzbischoff/tsmp: Time Series with Matrix Profile

Description

FLUSS is a Domain Agnostic Online Semantic Segmentation that uses the assumption that when few arc are crossing a given index point, means that there is a high probability of semantic change. This function is a wrap to fluss_cac() and fluss_extract().

Usage

fluss(.mp, num_segments = 1, exclusion_zone = NULL)

Arguments

.mp	a MatrixProfile object.
num_segments	an int. Number of segments to extract. Based on domain knowledge.
exclusion_zone	if a number will be used instead of embedded value. (Default is NULL).

Value

Returns the input .mp object new names: cac, corrected arc count and fluss with the location of semantic changes.

References

• Gharghabi S, Ding Y, Yeh C-CM, Kamgar K, Ulanova L, Keogh E. Matrix Profile VIII: Domain Agnostic Online Semantic Segmentation at Superhuman Performance Levels. In: 2017 IEEE International Conference on Data Mining (ICDM). IEEE; 2017. p. 117-26.

Website: https://sites.google.com/site/onlinesemanticsegmentation/

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

See Also

Other Semantic Segmentations: floss_cac(), floss_extract(), floss(), fluss_cac(), fluss_extract(), fluss_score()

Examples

data <- mp_fluss_data$tilt_abp$data[1:1000]
w <- 10
mp <- tsmp(data, window_size = w, verbose = 0)
mp <- fluss(mp, 2)

franzbischoff/tsmp documentation built on March 9, 2020, 6:01 a.m.