Extract candidate points of semantic changes.
floss_extract(.mpac, threshold = 1, exclusion_zone = NULL)
a TSMP object of class ArcCount
.
a number
. (Default is 1
). Set the maximum value for evaluating semantic changes.
This is data specific. It is advised to check what is 'normal' for your data.
if a number
will be used instead of embedded value. (Default is NULL
).
Returns the input .mp
object a new name floss
with the location of semantic
changes and floss_vals
with the normalized arc count value of the semantic change positions.
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/
Other Semantic Segmentations:
floss_cac()
,
floss()
,
fluss_cac()
,
fluss_extract()
,
fluss_score()
,
fluss()
data <- mp_fluss_data$tilt_abp$data[1:1000]
w <- 10
mp <- tsmp(data, window_size = w, verbose = 0)
mp <- fluss_cac(mp)
mp <- fluss_extract(mp, 2)