Type:	Package
Title:	Time Series Analysis Library
Version:	0.1.3
Description:	The library allows to perform a multivariate time series classification based on the use of Discrete Wavelet Transform for feature extraction, a step wise discriminant to select the most relevant features and finally, the use of a linear or quadratic discriminant for classification. Note that all these steps can be done separately which allows to implement new steps. Velasco, I., Sipols, A., de Blas, C. S., Pastor, L., & Bayona, S. (2023) <doi:10.1186/S12938-023-01079-X>. Percival, D. B., & Walden, A. T. (2000,ISBN:0521640687). Maharaj, E. A., & Alonso, A. M. (2014) <doi:10.1016/j.csda.2013.09.006>.
License:	Artistic-2.0
URL:	https://github.com/vg-lab/TSEAL
BugReports:	https://github.com/vg-lab/TSEAL/issues
Depends:	R (≥ 4.3.0)
Imports:	bigmemory, caret, checkmate, magrittr, MASS, methods, parallel, parallelly, pryr, statcomp, stats, synchronicity, utils, waveslim, wdm
Suggests:	spelling, testthat (≥ 3.0.0)
Config/testthat/edition:	3
Config/testthat/parallel:	faslse
Encoding:	UTF-8
Language:	en-US
RoxygenNote:	7.3.1
NeedsCompilation:	no
Packaged:	2024-07-02 10:10:03 UTC; ivan_
Author:	Iván Velasco [aut, cre, cph]
Maintainer:	Iván Velasco <ivan.velasco@urjc.es>
Repository:	CRAN
Date/Publication:	2024-07-02 12:10:07 UTC

K-Fold Cross Validation (KFCV)

Description

This function performs the K-Fold Cross Validation (KFCV) process with different types of input parameters.

Usage

KFCV(data, ...)

Arguments

Value

Not return value, used as generic function

See Also

• KFCV.array

• KFCV.MultiWaveAnalysis

KFCV

Description

Performs k-fold cross-validation where groups are chosen randomly. In case the value k is not divisor of the number of observations the last group will have nobs mod k observations.

Usage

## S3 method for class 'MultiWaveAnalysis'
KFCV(data, labels, method, k = 5L, returnClassification = FALSE, ...)

Arguments

Value

• if returnClassification is false return a object of class confusionMatrix

• if returnClassification is true, it returns a list containing an object of the confusionMatrix class and a vector with the classification result.

Examples

load(system.file("extdata/ECGExample.rda",package = "TSEAL"))
MWA <- MultiWaveAnalysis(ECGExample, "haar", features = c("var"))
MWADiscrim <- StepDiscrim(MWA, c(rep(1, 5), rep(2, 5)), 5,
              features = c("var"))
CM <- KFCV(MWADiscrim, c(rep(1, 5), rep(2, 5)), "linear", 5,
  returnClassification = FALSE
)

Generates and validates a discriminant model generated directly from the data.

Description

It generates and validates a discriminant model starting from the data. First , a MultiWaveAnalysis object is obtained according to the selected characteristics ,filter and levels. Then, the most important features are selected using a stepwise discriminant that allows to select a maximum number of variables (maxvars) or a minimum enhancement step (VStep). Finally, the model is trained using the subset of features and validated using K-Fold Cross Validation (KFCV).

Usage

## S3 method for class 'array'
KFCV(
  data,
  labels,
  f,
  method,
  maxvars,
  VStep,
  k = 5L,
  lev = 0L,
  features = c("Var", "Cor", "IQR", "PE", "DM"),
  returnClassification = FALSE,
  nCores = 0,
  ...
)

Arguments

Value

• if returnClassification is false return a object of class confusionMatrix

• if returnClassification is true, it returns a list containing an object of the confusionMatrix class and a vector with the classification result.

See Also

• LOOCV

• LOOCV.MultiWaveAnalysis

• availableFilters

• availableFeatures

Examples

load(system.file("extdata/ECGExample.rda",package = "TSEAL"))
labels <- c(rep(1, 5), rep(2, 5))
CM <- KFCV(ECGExample, labels, "haar", "linear",
  maxvars = 5,
  features = c("Var"), returnClassification = FALSE
)
# or with VStep
CMV <- KFCV(ECGExample, labels, "haar", "linear",
 k = 5,
 VStep = 5,
 features = c("Var"), returnClassification = FALSE
)

Leave-One-Out Cross Validation

Description

This function performs the Leave-One-Out Cross Validation (LOOCV) process with different types of input parameters.

Usage

LOOCV(data, ...)

Arguments

Value

Not return value, used as generic function

See Also

• LOOCV.array

• LOOCV.MultiWaveAnalysis

LOOCV

Description

Performs a leave-one-cross-validation (LOOCV) method on a MultiWaveAnalysis object. It is advisable to have selected a subset of all features (StepDiscrim,StepDiscrimV)

Usage

## S3 method for class 'MultiWaveAnalysis'
LOOCV(data, labels, method, returnClassification = FALSE, ...)

Arguments

Value

• if returnClassification is false return a object of class confusionMatrix

• if returnClassification is true, it returns a list containing an object of the confusionMatrix class and a vector with the classification result.

See Also

• LOOCV

• LOOCV.array

• StepDiscrim

• StepDiscrimV

Examples

load(system.file("extdata/ECGExample.rda",package = "TSEAL"))
MWA <- MultiWaveAnalysis(ECGExample, "haar", features = c("var"))
MWADiscrim <- StepDiscrim(MWA, c(rep(1, 5), rep(2, 5)), 5,
                          features = c("var"))
CM <- LOOCV(MWADiscrim, c(rep(1, 5), rep(2, 5)), "linear")

Generates and validates a discriminant model generated directly from the data.

Description

It generates and validates a discriminant model starting from the data. First, a MultiWaveAnalysis object is obtained according to the selected characteristics, filter and levels. Then, the most important features are selected using a stepwise discriminant that allows to select a maximum number of variables (maxvars) or a minimum enhancement step (VStep). Finally, the model is trained using the subset of features and validated using Leave-One-Out Cross Validation (LOOCV).

Usage

## S3 method for class 'array'
LOOCV(
  data,
  labels,
  f,
  method,
  maxvars,
  VStep,
  lev = 0,
  features = c("Var", "Cor", "IQR", "PE", "DM"),
  returnClassification = FALSE,
  nCores = 0,
  ...
)

Arguments

Value

• if returnClassification is false return a object of class confusionMatrix

• if returnClassification is true, it returns a list containing an object of the confusionMatrix class and a vector with the classification result.

See Also

• LOOCV

• LOOCV.MultiWaveAnalysis

• availableFilters

• availableFeatures

Examples

load(system.file("extdata/ECGExample.rda",package = "TSEAL"))
labels <- c(rep(1, 5), rep(2, 5))
CM <- LOOCV(ECGExample, labels, "haar", "linear",
  maxvars = 5,
  features = c("Var"), returnClassification = FALSE
)
# or with VStep
CMV <- LOOCV(ECGExample, labels, "haar", "linear",
 VStep = 5,
 features = c("Var", "Cor"), returnClassification = FALSE
)

Generate a MultiWave analysis

Description

Generates a multivariate analysis by calculating a series of features from the result of applying MODWT to the input data.

Usage

MultiWaveAnalysis(
  series,
  f,
  lev = 0,
  features = c("Var", "Cor", "IQR", "PE", "DM"),
  nCores = 0
)

Arguments

Value

A multivariate analysis with the characteristics indicated in the parameter features. This is an object of class MultiWaveAnalysis with contains * Features: A list with the computed features * StepSelection: A selection with the most discriminant features StepDiscrim * Observations: Number of total observations * NLevels: Number of levels selected for the decomposition process * Filter: Filter used in the decomposition process

See Also

• availableFilters

• availableFeatures

Examples

load(system.file("extdata/ECGExample.rda",package = "TSEAL"))
MWA <- MultiWaveAnalysis(ECGExample,
  f = "haar", lev = 0,
  features = c("Var", "Cor"), nCores = 0
)

Allows to select the same variables for a given StepDiscrim

Description

Allows to perform the same variable selection in a new MWA object starting from a MWA object with the variables already selected (it is advisable that the parameters of the MWA and of the selection are the same).

Usage

SameDiscrim(MWA, MWADiscrim)

Arguments

Value

An object of class MultiWaveAnalysis with the same variables selected as in the MWADiscrim object.

See Also

• StepDiscrim

• StepDiscrimV

Examples

load(system.file("extdata/ECGExample.rda",package = "TSEAL"))
# We simulate that the second series has been obtained after
Series1 <- ECGExample[, , 1:9]
Series2 <- ECGExample[, , 10, drop = FALSE]
MWA <- MultiWaveAnalysis(Series1, "haar", features = c("var"))
MWADiscrim <- StepDiscrim(MWA, c(rep(1, 5), rep(2, 4)), 5,
  features = c("var")
)

MWA2 <- MultiWaveAnalysis(Series2, "haar", features = c("var"))
MWA2Discrim <- SameDiscrim(MWA2, MWADiscrim)
# At this point MWA2Discrim has the same variables that MWADiscrim
# and can be used in a pretrained model with MWADiscrim

Select the most discriminating variables

Description

Stepwise discriminant analysis to determine the best subset of variables. Introduces variables so as to maximize at each step the Lawley-Hotelling trace (=Rao's V). This measure is proportional to the mean Mahalanobis distance.

Usage

StepDiscrim(
  MWA,
  labels,
  maxvars,
  features = c("Var", "Cor", "IQR", "PE", "DM"),
  nCores = 0
)

Arguments

Details

Based on StepDiscrim of R.E. Strauss

Value

A MultiWaveAnalysis object with the maxvars most discriminant variables. This object contains: * Features: A list with the initial computed features * StepSelection: The maxvars most discriminant variables * Observations: Number of total observations * NLevels: Number of levels selected for the decomposition process * filter: Filter used in the decomposition process

See Also

• MultiWaveAnalysis

• StepDiscrimV

Examples

load(system.file("extdata/ECGExample.rda",package = "TSEAL"))
MWA <- MultiWaveAnalysis(ECGExample, "haar", features = c("var"))
MWADiscrim <- StepDiscrim(
  MWA, c(rep(1, 5), rep(2, 5)), 5,
  c("Var")
)

Select the most discriminating variables

Description

Stepwise discriminant analysis to determine the best subset of variables. Introduces variables so as to maximize at each step the Lawley-Hotelling trace (=Rao's V). This measure is proportional to the mean Mahalanobis distance.The process ends when in one step the value of the Lawley-Hotelling trace is less than a given value.

Usage

StepDiscrimV(
  MWA,
  labels,
  VStep,
  features = c("Var", "Cor", "IQR", "PE", "DM"),
  nCores = 0
)

Arguments

Details

Based on StepDiscrim of R.E. Strauss

Value

A MultiWaveAnalysis object with the most discriminant variables. This Object contains: * Features: A list with the initial computed features * StepSelection: The most discriminant variables selected by this function * Observations: Number of total observations * NLevels: Number of levels selected for the decomposition process * filter: Filter used in the decomposition process

See Also

• MultiWaveAnalysis

• StepDiscrim

Examples

load(system.file("extdata/ECGExample.rda",package = "TSEAL"))
MWA <- MultiWaveAnalysis(ECGExample, "haar", features = c("var"))
MWADiscrim <- StepDiscrimV(
  MWA, c(rep(1, 5), rep(2, 5)), 0.1,
  c("Var")
)

availableFeatures

Description

Print the available features for the MultiWaveAnalysis and StepDiscrim

Usage

availableFeatures()

Value

A data.frame containing the name of the characteristics and their abbreviations for use in the code. For example, to use variances and correlations, the vector c("Var", "Cor") will be used.

See Also

• MultiWaveAnalysis

• StepDiscrim

• StepDiscrimV

Examples

availableFeatures()

availableFilters

Description

Print the available filters for the wave analysis

Usage

availableFilters()

Value

A data.frame containing all supported filters

See Also

• MultiWaveAnalysis

Examples

availableFilters()

Select the DWT level of decomposition based on wavelet filter, data series length and a user choice

Description

Select the DWT level of decomposition based on wavelet filter, data series length and a user choice

Usage

chooseLevel(choice, filter, N)

Arguments

Value

Number of level of decomposition based in selection criteria

References

Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis. Cambridge: Cambridge University Press.

Examples

lev <- chooseLevel("conservative", "haar", 8)

Classifies observations based on a pretrained model.

Description

This function allows to classify observations based on a pretrained model that could have been obtained in several ways (such as using the train model function). T

Usage

classify(data, ...)

Arguments

Value

A factor with predicted class of each observation

See Also

• trainModel

• classify.array

• classify.MultiWaveAnalysis

Classifies observations based on a pretrained model.

Description

This function allows to classify observations based on a pretrained model that could have been obtained in several ways (such as using the train model function).

Usage

## S3 method for class 'MultiWaveAnalysis'
classify(data, model, ...)

Arguments

Value

A factor with predicted class of each observation

See Also

• trainModel

Examples

load(system.file("extdata/ECGExample.rda",package = "TSEAL"))
# We simulate that the second series has been obtained after
Series1 <- ECGExample[, , 1:9]
Series2 <- ECGExample[, , 10, drop = FALSE]

# Training a discriminant model
MWA <- MultiWaveAnalysis(Series1, "haar", features = c("var"))
MWADiscrim <- StepDiscrim(MWA, c(rep(1, 5), rep(2, 4)), maxvars = 5,
                          features = c("var"))
model <- trainModel(MWADiscrim, c(rep(1, 5), rep(2, 4)), "linear")

# Using the discriminant trained on new data
MWA2 <- MultiWaveAnalysis(Series2, "haar", features = c("var"))
MWA2Discrim <- SameDiscrim(MWA2, MWADiscrim)
prediction <- classify(MWA2Discrim, model)

Classifies observations based on a pretrained model.

Description

This function allows to classify observations based on a pretrained model that could have been obtained in several ways (such as using the train model function).

Usage

## S3 method for class 'array'
classify(data, model, ...)

Arguments

Value

A factor with predicted class of each observation

See Also

• trainModel

Examples

load(system.file("extdata/ECGExample.rda",package = "TSEAL"))
# We simulate that the second series has been obtained after
Series1 <- ECGExample[, , 1:9]
Series2 <- ECGExample[, , 10, drop = FALSE]

# Training a discriminant model
MWA <- MultiWaveAnalysis(Series1, "haar", features = c("var"))
MWADiscrim <- StepDiscrim(MWA, c(rep(1, 5), rep(2, 4)), maxvars = 5,
                          features = c("var"))
model <- trainModel(MWADiscrim, c(rep(1, 5), rep(2, 4)), "linear")

# Using the discriminant trained on new data
prediction <- classify(Series2, model)

Extract observations from a MultiWaveAnalysis

Description

This function permits to extract certain observations from a MultiWaveAnalysis

Usage

extractSubset(MWA, indices)

Arguments

Value

A list with two elements:

• MWA: The MultiWaveAnalysis provided minus the extracted observations.

• MWAExtracted: A new MultiWaveAnalysis with the extracted observations

Examples

load(system.file("extdata/ECGExample.rda",package = "TSEAL"))
MWA <- MultiWaveAnalysis(ECGExample, "haar", features = "Var")
aux <- extractSubset(MWA, c(1, 2, 3))
MWATrain <- aux[[1]]
MWATest <- aux[[2]]

Generate StepDiscrim from raw data

Description

This function allows to obtain in a single step the complete MultiWaveAnalysis and the selection of the most discriminating variables of the MultiWaveAnalysis.

Usage

generateStepDiscrim(
  series,
  labels,
  f,
  maxvars,
  VStep,
  lev = 0,
  features = c("Var", "Cor", "IQR", "PE", "DM"),
  nCores = 0
)

Arguments

Value

A MultiWaveAnalysis with the most discriminant variables based on the features indicated.

See Also

• MultiWaveAnalysis

• StepDiscrim

• StepDiscrimV

Examples

load(system.file("extdata/ECGExample.rda",package = "TSEAL"))
# The dataset has the first 5 elements of class 1
# and the last 5 of class 2.
labels <- c(rep(1, 5), rep(2, 5))
MWADiscrim <- generateStepDiscrim(ECGExample, labels, "haar",
  features = c("Var"), maxvars = 5
)
# or using the VStep option
MWADiscrim <- generateStepDiscrim(ECGExample, labels, "haar",
 features = c("Var", "Cor"), VStep = 0.7
)

testFilters

Description

This function performs a test with a series of filters defined by the user, for the maximum number of variables determined. This function can be used to compare the performance of different filters with a different number of variables to be considered and the differences between a linear and a quadratic discriminant.

Usage

testFilters(
  series,
  labels,
  maxvars,
  filters = c("haar", "d4", "d6", "d8", "la8"),
  features = c("Var", "Cor", "IQR", "PE", "DM"),
  lev = 0
)

Arguments

Value

A list that each element contains:

• CM: confusion matrix with a particular configuration using LOOCV

• Classification: a vector with the raw classification result. "1" if the observation belongs to the population 1 and "2" if belongs to the population 2.

• NVars: the total numbers of variables have been taken into account in the classification process

• Method: type of classifier used.

• Filter: filter used in the MultiWave analysis process

• Features: vector containing the features taken into account

See Also

• LOOCV

• MultiWaveAnalysis

• StepDiscrim

• availableFilters

• availableFeatures

Examples

load(system.file("extdata/ECGExample.rda",package = "TSEAL"))
# The dataset has the first 5 elements of class 1
# and the last 5 of class 2.
labels <- c(rep(1, 5), rep(2, 5))
result <- testFilters(ECGExample, labels, features=c("var","cor"),
          filters= c("haar","d4"), maxvars = 3)

Computes a classification from a pretrained discriminant

Description

This function uses a pretrained linear discriminant to classify a set of test data. As output it returns a confusion matrix and optionally the raw classification result.

Usage

testModel(model, test, labels, returnClassification = FALSE, ...)

Arguments

Value

• if returnClassification is false return a object of class confusionMatrix

• if returnClassification is true, it returns a list containing an object of the confusionMatrix class and a vector with the classification result.

See Also

testModel

Examples

load(system.file("extdata/ECGExample.rda",package = "TSEAL"))
# The dataset has the first 5 elements of class 1
# and the last 5 of class 2.
labels <- c(rep(1, 5), rep(2, 5))
MWA <- generateStepDiscrim(ECGExample, labels, "haar", maxvars = 5, features = c("var"))
aux <- extractSubset(MWA, c(1, 2, 9, 10))
MWATest <- aux[[1]]
MWATrain <- aux[[2]]
ldaDiscriminant <- trainModel(MWATrain, labels[3:8], "linear")
CM <- testModel(ldaDiscriminant, MWATest, labels[c(1, 2, 9, 10)])

Generate a Discriminant Model

Description

This function allows training of a discriminant model using different inputs

Usage

trainModel(data, ...)

Arguments

Value

A trained discriminant model

See Also

• trainModel.array

• trainModel.MultiWaveAnalysis

Generates a discriminant model from an already generated "MultiWaveAnalysis".

Description

Generates a discriminant model from an already generated "MultiWaveAnalysis".

Usage

## S3 method for class 'MultiWaveAnalysis'
trainModel(data, labels, method, ...)

Arguments

Value

A discriminant model based on selected method. It can be an object of the class lda or qda.

See Also

• MultiWaveAnalysis

• StepDiscrim

• StepDiscrimV

Examples

load(system.file("extdata/ECGExample.rda",package = "TSEAL"))
MWA <- MultiWaveAnalysis(ECGExample, "d6", features = c("Var"))
MWADiscrim <- StepDiscrim(MWA, c(rep(1, 5), rep(2, 5)), 5,
  features = c("Var")
)
model <- trainModel(MWADiscrim, c(rep(1, 5), rep(2, 5)), "linear")

Generates a discriminant model from training data.

Description

It generates a discriminant model starting from the training data, which must be provided in 2 groups depending on their classification. The method first obtains the variances and correlations using MODWT, the f filter is applied with a number of levels lev. Then a subset of all the generated features will be obtained by means of a stepwise discriminant, which can be driven by a maximum number of features or by a minimum metric to be met. Finally, the selected discriminant model is trained with the subset obtained.

Usage

## S3 method for class 'array'
trainModel(
  data,
  labels,
  f,
  method,
  maxvars,
  VStep,
  lev = 0,
  features = c("Var", "Cor", "IQR", "PE", "DM"),
  nCores = 0,
  ...
)

Arguments

Value

A discriminant model object (lda or qda)

See Also

• StepDiscrim

• StepDiscrimV

• trainModel

Examples

load(system.file("extdata/ECGExample.rda",package = "TSEAL"))
# The dataset has the first 5 elements of class 1 and the last 5 of class 2.
labels <- c(rep(1, 5), rep(2, 5))
model <- trainModel(ECGExample, labels, "d6", "linear",
  maxvars = 5, features = c("Var")
)
# or using VStep
modelV <- trainModel(ECGExample, labels, "d6", "linear",
    VStep = 14.5, features = c("Var")
)