# ac_corrected: Transforms the data X to account for autocorrelation. In anomaly: Detecting Anomalies in Data

## Description

Transforms the data X to account for autocorrelation by centring and scaling. It uses the transformation X_{i}^{'} = \frac{X_{i}-μ_{i}}{k_{i}σ_{i}}, were μ_{i} and σ_{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{≤ft( \frac{1+φ_{i}}{1-φ_{i}} \right)}, with φ_{i} a robust estimate for the autocorrelation at lag 1, is used to account for AR(1) structure in the noise.

## Usage

 1 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. The time series data classes ts, xts, and zoo are also supported.

## Value

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

## Examples

  1 2 3 4 5 6 7 8 9 10 11 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)) 

anomaly documentation built on Oct. 21, 2021, 1:06 a.m.