outlier_detection: Constructs the outlier detection procedure of López-Oriona...

In mlmts: Machine Learning Algorithms for Multivariate Time Series

Constructs the outlier detection procedure of López-Oriona and Vilar (2021)

Description

outlier_detection computes the outlier detection method for MTS proposed by \insertCitelopez2021outlier;textualmlmts.

Usage

outlier_detection(X, levels = c(0.1, 0.5, 0.9), alpha = NULL)

Arguments

X	A list of MTS (numerical matrices).
levels	The set of probability levels to compute the QCD-estimates.
alpha	The desired rate of outliers to detect (a real number between 0 and 1).

Details

This function performs outlier detection according to the procedure proposed by \insertCitelopez2021outlier;textualmlmts. Specifically, each MTS in the original set is described by means of a multivariate functional datum by using an estimate of its quantile cross- spectral density. Given the corresponding set of multivariate functional data, the functional depth of each object is computed. Based on depth computations, the outlying elements are the objects with low values for the depths.

Value

A list with two elements:

• Depths. The functional depths associated with elements in X, sorted in increasing order.

• Indexes. The corresponding indexes associated with the vector Depths.

Author(s)

Ángel López-Oriona, José A. Vilar

References

\insertRef

lopez2021outliermlmts

See Also

dis_qcd

Examples

outliers <- outlier_detection(SyntheticData2$data[c(1 : 3, 65)])
outliers$Indexes[1] # The first outlying MTS in dataset SyntheticData2
outliers$Depths[1] # The corresponding value for the depths

mlmts documentation built on Sept. 11, 2024, 6:41 p.m.