dis_var_1 {mlmts} | R Documentation |
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 Piccolo (1990).
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 |
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
Piccolo D (1990). “A distance measure for classifying ARIMA models.” Journal of time series analysis, 11(2), 153–164.
See Also
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