Given a collection of MTS, the function returns the pairwise distance matrix,
where the distance between two MTS \(\boldsymbol X_T\) and \(\boldsymbol Y_T\) is defined
as $$d_{MCC}(\boldsymbol X_{T}, \boldsymbol Y_{T})=\Big|\Big|vec\big(\widehat{\boldsymbol \Theta}^{\boldsymbol X_T}\big)
-vec\big(\widehat{\boldsymbol \Theta}^{\boldsymbol Y_T}\big)\Big|\Big|,$$
where \(\widehat{\boldsymbol \Theta}^{\boldsymbol X_T}\) and \(\widehat{\boldsymbol \Theta}^{\boldsymbol Y_T}\)
are matrices containing pairwise estimated maximal cross-correlations
(in absolute value) for series \(\boldsymbol X_T\) and \(\boldsymbol Y_T\), respectively,
and the operator \(vec(\cdot)\) creates a vector by concatenating the columns
of the matrix received as input. If we use the function to perform dimensionality
reduction (features = TRUE), then for a given series \(\boldsymbol X_T\),
a new matrix \(\widehat{\boldsymbol \Theta}^{\boldsymbol X_T}_\delta\) is
constructed by keeping the entries of matrix \(\widehat{\boldsymbol \Theta}^{\boldsymbol X_T}\)
which are above \(\delta\) (and setting all the remaining entries to zero).
The connected components of the graph defined by matrix
\(\widehat{\boldsymbol \Theta}^{\boldsymbol X_T}_\delta\) are computed
along with their corresponding centers (variables). Function dis_mcc
returns the reduced counterpart of \(\boldsymbol X_T\), which is constructed
from \(\boldsymbol X_T\) by removing all the variables which were not
selected as centers of the corresponding components.