Learn R Programming

TSEAL (version 0.1.3)

Time Series Analysis Library

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) . Percival, D. B., & Walden, A. T. (2000,ISBN:0521640687). Maharaj, E. A., & Alonso, A. M. (2014) .

Copy Link

Version

Install

install.packages('TSEAL')

Monthly Downloads

133

Version

0.1.3

License

Artistic-2.0

Issues

Pull Requests

Stars

Forks

Maintainer

Iván Velasco

Last Published

July 2nd, 2024

Functions in TSEAL (0.1.3)

chooseLevel

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

Computes a classification from a pretrained discriminant
classify.array

Classifies observations based on a pretrained model.
trainModel.array

Generates a discriminant model from training data.
trainModel.MultiWaveAnalysis

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

Generate a Discriminant Model
StepDiscrimV

Select the most discriminating variables
KFCV.MultiWaveAnalysis

KFCV
LOOCV.MultiWaveAnalysis

LOOCV
StepDiscrim

Select the most discriminating variables
LOOCV

Leave-One-Out Cross Validation
SameDiscrim

Allows to select the same variables for a given StepDiscrim
KFCV.array

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

availableFilters
classify.MultiWaveAnalysis

Classifies observations based on a pretrained model.
MultiWaveAnalysis

Generate a MultiWave analysis
LOOCV.array

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

K-Fold Cross Validation (KFCV)
availableFeatures

availableFeatures
classify

Classifies observations based on a pretrained model.
testFilters

testFilters
extractSubset

Extract observations from a MultiWaveAnalysis
generateStepDiscrim

Generate StepDiscrim from raw data