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
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TSEAL.pdf |TSEAL.html✨
TSEAL/json (API)

Peer review:

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

On CRAN:

15 exports 1.10 score 91 dependencies 198 downloads

Last updated 3 months agofrom:425531b1a0. Checks:OK: 1 WARNING: 6. Indexed: yes.

Exports:availableFeaturesavailableFilterschooseLevelclassifyextractSubsetgenerateStepDiscrimKFCVLOOCVMultiWaveAnalysisSameDiscrimStepDiscrimStepDiscrimVtestFilterstestModeltrainModel

Dependencies:backportsBHbigmemorybigmemory.sricaretcheckmateclasscliclockcodetoolscolorspacecpp11crayondata.tablediagramdigestdplyre1071fansifarverforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlobstrlubridatemagrittrMASSMatrixmgcvModelMetricsmultitapermunsellnlmennetnumDerivparallellypillarpkgconfigplyrprettyunitspROCprodlimprogressrproxypryrpurrrR6RColorBrewerRcpprecipesreshape2rlangrpartscalesshapeSQUAREMstatcompstringistringrsurvivalsynchronicitytibbletidyrtidyselecttimechangetimeDatetzdbutf8uuidvctrsviridisLitewaveslimwdmwithrzoo