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>.

Version: 0.1.0
Imports: stats, Rlibeemd, tseries, forecast, fGarch, aTSA, FinTS, LSTS, earth, caret, neuralnet, e1071, pso
Published: 2023-04-07
DOI: 10.32614/CRAN.package.CEEMDANML
Author: Mr. Sandip Garai [aut, cre], Dr. Ranjit Kumar Paul [aut], Dr. Md Yeasin [aut]
Maintainer: Mr. Sandip Garai <sandipnicksandy at gmail.com>
License: GPL-3
NeedsCompilation: no
CRAN checks: CEEMDANML results


Reference manual: CEEMDANML.pdf


Package source: CEEMDANML_0.1.0.tar.gz
Windows binaries: r-devel: CEEMDANML_0.1.0.zip, r-release: CEEMDANML_0.1.0.zip, r-oldrel: CEEMDANML_0.1.0.zip
macOS binaries: r-release (arm64): CEEMDANML_0.1.0.tgz, r-oldrel (arm64): CEEMDANML_0.1.0.tgz, r-release (x86_64): CEEMDANML_0.1.0.tgz, r-oldrel (x86_64): CEEMDANML_0.1.0.tgz

Reverse dependencies:

Reverse imports: CompareMultipleModels, WaveletMLbestFL


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