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WaveletGBM  

Wavelet Based Gradient Boosting Method
View on CRAN: Click here


Download and install WaveletGBM package within the R console
Install from CRAN:
install.packages("WaveletGBM")

Install from Github:
library("remotes")
install_github("cran/WaveletGBM")

Install by package version:
library("remotes")
install_version("WaveletGBM", "0.1.0")



Attach the package and use:
library("WaveletGBM")
Maintained by
Dr. Ranjit Kumar Paul
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-04-07
Latest Update: 2023-04-07
Description:
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>.
How to cite:
Dr. Ranjit Kumar Paul (2023). WaveletGBM: Wavelet Based Gradient Boosting Method. R package version 0.1.0, https://cran.r-project.org/web/packages/WaveletGBM. Accessed 03 Oct. 2026.
Previous versions and publish date:
No previous versions
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View WaveletGBM citation profile
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Complete documentation for WaveletGBM
Functions, R codes and Examples using the WaveletGBM R package
Full WaveletGBM package functions and examples
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