dis_dtw_2: Constructs a pairwise distance matrix based on multivariate...

In mlmts: Machine Learning Algorithms for Multivariate Time Series

Constructs a pairwise distance matrix based on multivariate dynamic time warping

Description

dis_dtw_2 returns a pairwise distance matrix based on one of the multivariate extensions of the well-known dynamic time warping distance \insertCiteshokoohi2017generalizingmlmts.

Usage

dis_dtw_2(X, normalization = FALSE, ...)

Arguments

X	A list of MTS (numerical matrices).
normalization	Logical. If normalization = TRUE (default), the normalized distance is computed. Otherwise (default), no normalization is taken into account
...	Additional parameters for the function. See dtw.

Details

Given a collection of MTS, the function returns the pairwise distance matrix, where the distance between two MTS is defined as the multivariate extension of the dynamic time warping distance which forces all dimensions to warp identically, in a single warping matrix.

Value

The computed pairwise distance matrix.

Author(s)

Ángel López-Oriona, José A. Vilar

References

\insertRef

shokoohi2017generalizingmlmts

See Also

dis_dtw_2, dis_mahalanobis_dtw

Examples

toy_dataset <- AtrialFibrillation$data[1 : 10] # Selecting the first 10 MTS from the
# dataset AtrialFibrillation
distance_matrix <- dis_dtw_2(toy_dataset) # Computing the pairwise
# distance matrix based on the distance dis_dtw1 without normalization
distance_matrix_normalized <- dis_dtw_2(toy_dataset, normalization = TRUE)
# Computing the pairwise distance matrix based
# distance matrix based on the distance dis_dtw1 with normalization

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