dis_var_1: Constructs a pairwise distance matrix based on the estimated...

In mlmts: Machine Learning Algorithms for Multivariate Time Series

Constructs a pairwise distance matrix based on the estimated VAR coefficients of the series

Description

dis_cor returns a pairwise distance matrix based on a generalization of the dissimilarity introduced by \insertCitepiccolo1990distance;textualmlmts.

Usage

dis_var_1(X, max_p = 1, criterion = "AIC", features = FALSE)

Arguments

X	A list of MTS (numerical matrices).
max_p	The maximum order considered with respect to the fitting of VAR models.
criterion	The criterion used to determine the VAR order.
features	Logical. If features = FALSE (default), a distance matrix is returned. Otherwise, the function returns a dataset of feature vectors.

Details

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_{VAR}(\boldsymbol X_T, \boldsymbol Y_T)=||\widehat{\boldsymbol \theta}^{\boldsymbol X_T}_{VAR}- \widehat{\boldsymbol \theta}^{\boldsymbol Y_T}_{VAR}||,

where \widehat{\boldsymbol \theta}^{\boldsymbol X_T}_{VAR} and \widehat{\boldsymbol \theta}^{\boldsymbol Y_T}_{VAR} are vectors containing the estimated VAR parameters for \boldsymbol X_T and \boldsymbol Y_T, respectively. If VAR models of different orders are fitted to \boldsymbol X_T and \boldsymbol Y_T, then the shortest vector is padded with zeros until it reaches the length of the longest vector.

Value

If features = FALSE (default), returns a distance matrix based on the distance d_{COR}. Otherwise, the function returns a dataset of feature vectors, i.e., each row in the dataset contains the features employed to compute the distance d_{VAR}.

Author(s)

Ángel López-Oriona, José A. Vilar

References

\insertRef

piccolo1990distancemlmts

See Also

dis_var_2, diss.AR.PIC

Examples

toy_dataset <- Libras$data[1 : 2] # Selecting the first 2 MTS from the
# dataset Libras
distance_matrix <- dis_var_1(toy_dataset) # Computing the pairwise
# distance matrix based on the distance dis_var_1
feature_dataset <- dis_var_1(toy_dataset, features = TRUE) # Computing
# the corresponding dataset of features

mlmts documentation built on Sept. 11, 2024, 6:41 p.m.