f4_classifier {mlmts}

Constructs the F4 classifier of López-Oriona and Vilar (2021)

Description

f4_classifier computes the F4 classifier for MTS proposed by Lopez-Oriona and Vilar (2021).

Usage

f4_classifier(
  training_data,
  new_data = NULL,
  classes,
  levels = c(0.1, 0.5, 0.9),
  cv_folds = 5,
  var_rate = 0.9
)

Arguments

training_data

A list of MTS constituting the training set to fit classifier F4.

new_data

A list of MTS for which the class labels have to be predicted.

classes

A vector containing the class labels associated with the elements in training_data.

levels

The set of probability levels to compute the QCD-estimates.

cv_folds

The number of folds concerning the cross-validation procedure used to fit F4 with respect to training_data.

var_rate

Rate of desired variability to select the principal components associated with the QCD-based features.

Details

This function constructs the classifier F4 of . Given a set of MTS with associated class labels, estimates of the quantile cross-spectral density (QCD) and the maximum overlap discrete wavelet transform (MODWT) are first computed for each series. Then Principal Components Analysis (PCA) is applied over the dataset of QCD-based features and a given number of principal components are retained according to a criterion of explained variability. Next, each series is decribed by means of the concatenation of the QCD-based transformed features and the MODWT-based features. Finally, a traditional random forest classifier is executed in the resulting dataset.

Value

If new_data = NULL (default), returns a fitted model of class train (see train). Otherwise, the function returns the predicted class labels for the elements in new_data.

Author(s)

Ángel López-Oriona, José A. Vilar

References

Lopez-Oriona A, Vilar JA (2021). “F4: An All-Purpose Tool for Multivariate Time Series Classification.” Mathematics, 9(23), 3051.

Examples

predictions <- f4_classifier(training_data = Libras$data[1 : 20],
new_data = Libras$data[181 : 200], classes = Libras$classes[181 : 200])
# Computing the predictions for the test set of dataset Libras