dis_var_2: Model-based dissimilarity proposed by Maharaj (1999)

In mlmts: Machine Learning Algorithms for Multivariate Time Series

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 \insertCitemaharaj1999comparisonmlmts.

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

\insertRef

maharaj1999comparisonmlmts

See Also

dis_var_1, diss.AR.MAH

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

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