find_discords_hotsax: Finds a discord (i.e. time series anomaly) with HOT-SAX....

In jMotif/jmotif-R: Time Series Analysis Toolkit Based on Symbolic Aggregate Discretization, i.e. SAX

Finds a discord (i.e. time series anomaly) with HOT-SAX. Usually works the best with lower sizes of discretization parameters: PAA and Alphabet.

Description

Finds a discord (i.e. time series anomaly) with HOT-SAX. Usually works the best with lower sizes of discretization parameters: PAA and Alphabet.

Usage

find_discords_hotsax(ts, w_size, paa_size, a_size, n_threshold, discords_num)

Arguments

ts	the input timeseries.
w_size	the sliding window size.
paa_size	the PAA size.
a_size	the alphabet size.
n_threshold	the normalization threshold.
discords_num	the number of discords to report.

References

Keogh, E., Lin, J., Fu, A., HOT SAX: Efficiently finding the most unusual time series subsequence. Proceeding ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining

Examples

discords = find_discords_hotsax(ecg0606, 100, 3, 3, 0.01, 1)
plot(ecg0606, type = "l", col = "cornflowerblue", main = "ECG 0606")
lines(x=c(discords[1,2]:(discords[1,2]+100)),
   y=ecg0606[discords[1,2]:(discords[1,2]+100)], col="red")

jMotif/jmotif-R documentation built on Sept. 27, 2022, 4:31 p.m.