| dis_var_2 {mlmts} | R Documentation |
Model-based dissimilarity proposed by Maharaj (1999)
Description
dis_var_2 returns a pairwise distance matrix based on testing whether
each pair of series are or not generated from the same VARMA model
(Maharaj 1999).
Usage
dis_var_2(X, max_p = 2, criterion = "BIC")
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. |
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 1-p, where p is the p-value of the test of hypothesis proposed
by . This test is based on checking the equality of the underlying VARMA models
of both series. The VARMA structures are approximated by truncated VAR(\infty)
models with a common order k = \max{(k_x, k_y)}, where k_x and k_y
are determined by the BIC or AIC criterion. The VAR coefficients are automatically fitted.
The dissimilarity between both series is given by 1-p because this quantity
is expected to take larger values the more different both generating processes are.
The procedure is able to compare two dependent MTS.
Value
The computed pairwise distance matrix.
Author(s)
Ángel López-Oriona, José A. Vilar
References
Maharaj EA (1999). “Comparison and classification of stationary multivariate time series.” Pattern Recognition, 32(7), 1129–1138.
See Also
Examples
toy_dataset <- Libras$data[c(1, 2)] # Selecting the first two MTS from the
# dataset Libras
distance_matrix <- dis_var_2(toy_dataset, max_p = 1) # Computing the pairwise
# distance matrix based on the distance dis_var_2