Package: TSEAL 0.1.3

Iván Velasco

TSEAL:Time Series Analysis Library

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>.

Authors:Iván Velasco [aut, cre, cph]

TSEAL_0.1.3.tar.gz
TSEAL_0.1.3.tar.gz(r-4.5-noble)TSEAL_0.1.3.tar.gz(r-4.4-noble)
TSEAL_0.1.3.tgz(r-4.4-emscripten)TSEAL_0.1.3.tgz(r-4.3-emscripten)
TSEAL.pdf |TSEAL.html✨
TSEAL/json (API)

# InstallTSEAL in R:

install.packages('TSEAL',repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/vg-lab/tseal/issues

15 exports 0.23 score 92 dependencies 445 downloads

Last updated 3 days agofrom:4f62e3dc7c

Exports:availableFeatures availableFilters chooseLevel classify extractSubset generateStepDiscrim KFCV LOOCV MultiWaveAnalysis SameDiscrim StepDiscrim StepDiscrimV testFilters testModel trainModel

Dependencies:backports BH bigmemory bigmemory.sri caret checkmate class cli clock codetools colorspace cpp11 crayon data.table diagram digest dplyr e1071 ellipsis fansi farver foreach future future.apply generics ggplot2 globals glue gower gtable hardhat ipred isoband iterators KernSmooth labeling lattice lava lifecycle listenv lobstr lubridate magrittr MASS Matrix mgcv ModelMetrics multitaper munsell nlme nnet numDeriv parallelly pillar pkgconfig plyr prettyunits pROC prodlim progressr proxy pryr purrr R6 RColorBrewer Rcpp recipes reshape2 rlang rpart scales shape SQUAREM statcomp stringi stringr survival synchronicity tibble tidyr tidyselect timechange timeDate tzdb utf8 uuid vctrs viridisLite waveslim wdm withr zoo

Help page	Topics
availableFeatures	availableFeatures
availableFilters	availableFilters
Select the DWT level of decomposition based on wavelet filter, data series length and a user choice	chooseLevel
Classifies observations based on a pretrained model.	classify
Classifies observations based on a pretrained model.	classify.array
Classifies observations based on a pretrained model.	classify.MultiWaveAnalysis
Extract observations from a MultiWaveAnalysis	extractSubset
Generate StepDiscrim from raw data	generateStepDiscrim
K-Fold Cross Validation (KFCV)	KFCV
Generates and validates a discriminant model generated directly from the data.	KFCV.array
KFCV	KFCV.MultiWaveAnalysis
Leave-One-Out Cross Validation	LOOCV
Generates and validates a discriminant model generated directly from the data.	LOOCV.array
LOOCV	LOOCV.MultiWaveAnalysis
Generate a MultiWave analysis	MultiWaveAnalysis
Allows to select the same variables for a given StepDiscrim	SameDiscrim
Select the most discriminating variables	StepDiscrim
Select the most discriminating variables	StepDiscrimV
testFilters	testFilters
Computes a classification from a pretrained discriminant	testModel
Generate a Discriminant Model	trainModel
Generates a discriminant model from training data.	trainModel.array
Generates a discriminant model from an already generated "MultiWaveAnalysis".	trainModel.MultiWaveAnalysis

Package: TSEAL 0.1.3

TSEAL:Time Series Analysis Library

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