anomaly (version 2.0.1)

ac_corrected: Transforms the data X to account for autocorrelation.

Description

Transforms the data X to account for autocorrelation by centring and scaling. It uses the transformation \( X_{i}^{'} = \frac{X_{i}-\mu_{i}}{k_{i}\sigma_{i}}\), were \(\mu_{i}\) and \(\sigma_{i}\) are robust estimates for the mean and standard deviation of each variate (column), \(X_{i}\), of X. The estimates are calculated using the median and median absolute deviation. The scaling \(k_{i} = \surd{\left( \frac{1+\phi_{i}}{1-\phi_{i}} \right)}\), with \(\phi_{i}\) a robust estimate for the autocorrelation at lag 1, is used to account for AR(1) structure in the noise.

Usage

ac_corrected(X)

Arguments

X

A numeric matrix containing the potentially multivariate data to be transformed. Each column corresponds to a component and each row to an observation.

Value

A numeric matrix of the same dimension as X containing the transformed data.

Examples

Run this code
# NOT RUN {
library(anomaly)
# generate some multivariate data
set.seed(0)
X<-simulate(n=1000,p=4,mu=10,locations=c(200,400,600),
            duration=100,proportions=c(0.25,0.5,0.75))
# compare the medians of each variate and transformed variate
head(apply(X,2,median))
head(apply(ac_corrected(X),2,median))
# compare the variances of each variate and transformed variate
head(apply(X,2,var))
head(apply(ac_corrected(X),2,var))

# }

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