anomaly: Anomalous time-series detection

In pli2016/anomalous: Unusual Time Series Detection

Description

anomaly is a function for detecting unusual (i.e., anomalous, abnormal, outlier) time-series in a set of time-series.

Usage

  anomaly(x, n = 10, method = "hdr", robust = TRUE, plot = TRUE, 
  labels = TRUE, col)

Arguments

x	A feature matrix returned by 'tsmeasures' function
n	Number of unusual time-series to return
method	Bivariate outlier detection method used for detecting high density regions of the first two principle components extracted from the time-series
robust	If TRUE a robust PCA will be used for feature extraction
plot	If TRUE, a visualization of the anomalous time-series in the first two principle compoents will be shown
labels	If TRUE, labels will be added to give the anomlous time series an ordering index.
col	A vector of length 2 giving the colours for the first and second set of points respectively (and the corresponding axes). If a single colour is specified it will be used for both sets. If missing the default colour is used.

Value

A vector of n most unusual time-series and a matrix of principal component scores

Author(s)

Rob J Hyndman, Earo Wang, Nikolay Laptev

See Also

hdr.2d,

Examples

  y <- tsmeasures(dat0, window = 30)
  anomaly(y, n = 2, method = "hdr") 

pli2016/anomalous documentation built on May 25, 2019, 8:22 a.m.