Description Usage Arguments Value Examples

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.

1 | ```
ac_corrected(X)
``` |

`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. |

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

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))
``` |

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