outliers.hdts: Multivariate Outlier Detection

Description Usage Arguments Value References Examples

View source: R/outliers.hdts.R

Description

Outlier detection in high dimensional time series by using projections as in Galeano, Peña and Tsay (2006).

Usage

1
outliers.hdts(x, r.max, type)

Arguments

x

T by k data matrix: T data points in rows with each row being data at a given time point, and k time series in columns.

r.max

The maximum number of factors including stationary and non-stationary.

type

The type of series, i.e., 1 if stationary or 2 if nonstationary.

Value

A list containing:

References

Galeano, P., Peña, D., and Tsay, R. S. (2006). Outlier detection in multivariate time series by projection pursuit. Journal of the American Statistical Association, 101(474), 654-669.

Examples

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data(TaiwanAirBox032017)
output <- outliers.hdts(as.matrix(TaiwanAirBox032017[1:100,1:3]), r.max = 1, type =2)

SLBDD documentation built on March 27, 2021, 9:07 a.m.

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