Package: CEEMDANML 0.1.0

CEEMDANML: CEEMDAN Decomposition Based Hybrid Machine Learning Models

Noise in the time-series data significantly affects the accuracy of the Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression are considered here). Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the time series data into sub-series and help to improve the model performance. The models can achieve higher prediction accuracy than the traditional ML models. Two models have been provided here for time series forecasting. More information may be obtained from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202>.

Authors:Mr. Sandip Garai [aut, cre], Dr. Ranjit Kumar Paul [aut], Dr. Md Yeasin [aut]

CEEMDANML_0.1.0.tar.gz
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CEEMDANML.pdf |CEEMDANML.html
CEEMDANML/json (API)

# Install 'CEEMDANML' in R:
install.packages('CEEMDANML', repos = c('https://sandipgarai.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.78 score 2 packages 190 downloads 2 exports 111 dependencies

Last updated 2 years agofrom:6ca7dfa85a. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 29 2024
R-4.5-winOKOct 29 2024
R-4.5-linuxOKOct 29 2024
R-4.4-winOKOct 29 2024
R-4.4-macOKOct 29 2024
R-4.3-winOKOct 29 2024
R-4.3-macOKOct 29 2024

Exports:carigaancarigas

Dependencies:aTSAcaretclasscliclockcodetoolscolorspacecpp11curlcvardata.tableDerivdiagramdigestdplyre1071earthfansifarverfastICAfBasicsfGarchFinTSforeachforecastFormulafracdifffuturefuture.applygbutilsgenericsggplot2globalsgluegowergssgtablehardhatipredisobanditeratorsjsonliteKernSmoothlabelinglatticelavalifecyclelistenvlmtestLSTSlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellneuralnetnlmennetnumDerivparallellypatchworkpillarpkgconfigplotmoplotrixplyrpROCprodlimprogressrproxypsopurrrquadprogquantmodR6rbibutilsRColorBrewerRcppRcppArmadilloRdpackrecipesreshape2rlangRlibeemdrpartscalesshapespatialSQUAREMstablediststringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetimeSeriestseriesTTRtzdburcautf8vctrsviridisLitewithrxtszoo