WaveletGBM: Wavelet Based Gradient Boosting Method

Wavelet decomposition method is very useful for modelling noisy time series data. Wavelet decomposition using 'haar' algorithm has been implemented to developed hybrid Wavelet GBM (Gradient Boosting Method) model for time series forecasting using algorithm by Anjoy and Paul (2017) <doi:10.1007/s00521-017-3289-9>.

Version: 0.1.0
Imports: caret, dplyr, caretForecast, Metrics, tseries, stats, wavelets, gbm
Published: 2023-04-07
Author: Dr. Ranjit Kumar Paul [aut, cre], Dr. Md Yeasin [aut]
Maintainer: Dr. Ranjit Kumar Paul <ranjitstat at gmail.com>
License: GPL-3
NeedsCompilation: no
CRAN checks: WaveletGBM results

Documentation:

Reference manual: WaveletGBM.pdf

Downloads:

Package source: WaveletGBM_0.1.0.tar.gz
Windows binaries: r-devel: WaveletGBM_0.1.0.zip, r-release: WaveletGBM_0.1.0.zip, r-oldrel: WaveletGBM_0.1.0.zip
macOS binaries: r-release (arm64): WaveletGBM_0.1.0.tgz, r-oldrel (arm64): WaveletGBM_0.1.0.tgz, r-release (x86_64): WaveletGBM_0.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=WaveletGBM to link to this page.