Performs multivariate time series outlier ensembling.

mv_tsout_ens(
  x,
  m1 = NULL,
  ncomp = 2,
  sds = 1,
  rept = 1,
  compr = 2,
  rat = 0.05,
  fast = TRUE
)

Arguments

x: A data frame or a matrix object containing a multivariate time series
m1: Variable indicating dimension reduction methods. Default is set to using all 4 methods: PCA, DOBIN, ICS and ICA.
ncomp: The number of components for each dimension reduction method. Default is set to 2.
sds: The random seed for generating a no-outlier time series.
rept: The number of repetitions for generating a no-outlier time series.
compr: To adjust for multiple testing, the results of the ensemble are compared with the results of a time series without outliers. If compr =1, a time series is simulated as in simulate_comp_ts without outliers. If compr = 2, the top outliers are removed from the outlier series and interpolated values are used for those time points. If compr = 3 both methods of simulation are used for comparison.
rat: A comparison is done with the outliers removed time series. The variable rat denotes the ratio of outliers to be removed as a proportion of the whole dataset for this comparison.
fast: For faster computation skip ICS decomposition method.

Value

A list with the following components:

outliers: The outliers detected from the multivariate ensemble after comparing with the comparison time series without outliers.
all: All the outliers detected from the multivariate ensemble.
outmat: A matrix with outlier scores organised by outlier method.
wts: The weights of the outlier detection methods.
pca_loadings: The basis vectors from PCA.
dobin_loadings: The basis vectors from DOBIN. See R package dobin for more details.
ics_loadings: The basis vectors from ICS. See R package ICS for more details.
ica_loadings: The basis vectors from Independent Component Analysis.
decomp_wts: Each decomposition method has several components. For example if ncomp=2, then there are 2 PC components, 2 DOBIn components, etc ... The weight of each component is given different and depends on the decomposition method. These weights are given in decomp_wts.
outmat4D: A 4D array with outlier scores organised by outlier method, decomposition method, components for each decomposition method and time.

Examples

if (FALSE) {
set.seed(100)
n <- 600
x <- sample(1:100, n, replace=TRUE)
x[25] <- 200
x[320] <- 300
x2 <- sample(1:100, n, replace=TRUE)
x3 <- sample(1:100, n, replace=TRUE)
x4 <- sample(1:100, n, replace=TRUE)
X <- cbind.data.frame(x, x2, x3, x4)
out <- mv_tsout_ens(X, m1=c(1,2,4), compr=2)
}