dis_hwl {mlmts}

Constructs a pairwise distance matrix based on feature extraction

Description

dis_hwl returns a pairwise distance matrix based on the feature extraction procedure proposed by Hyndman et al. (2015).

Usage

dis_hwl(X, features = FALSE)

Arguments

X

A list of MTS (numerical matrices).

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 is defined as the Euclidean distance between the corresponding feature vectors

Value

If features = FALSE (default), returns a distance matrix based on the distance d_{HWL}. 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_{HWL}.

Author(s)

Ángel López-Oriona, José A. Vilar

References

Hyndman RJ, Wang E, Laptev N (2015). “Large-scale unusual time series detection.” In 2015 IEEE international conference on data mining workshop (ICDMW), 1616–1619. IEEE.

Examples

toy_dataset <- AtrialFibrillation$data[1 : 10] # Selecting the first 10 MTS from the
# dataset AtrialFibrillation
distance_matrix <- dis_hwl(toy_dataset) # Computing the pairwise
# distance matrix based on the distance dis_hwl
#' feature_dataset <- dis_hwl(toy_dataset, features = TRUE) # Computing
# the corresponding dataset of features