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WaveletRF  

Wavelet-RF Hybrid Model for Time Series Forecasting
View on CRAN: Click here


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

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

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



Attach the package and use:
library("WaveletRF")
Maintained by
Ranjit Kumar Paul
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-02-22
Latest Update: 2022-02-22
Description:
The Wavelet Decomposition followed by Random Forest Regression (RF) models have been applied for time series forecasting. The maximum overlap discrete wavelet transform (MODWT) algorithm was chosen as it works for any length of the series. The series is first divided into training and testing sets. In each of the wavelet decomposed series, thesupervised machine learning approach namely random forest was employed to train the model. This package also provides accuracy metrics in the form of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Ding et al. (2021) <doi:10.1007/s11356-020-12298-3>.
How to cite:
Ranjit Kumar Paul (2022). WaveletRF: Wavelet-RF Hybrid Model for Time Series Forecasting. R package version 0.1.0, https://cran.r-project.org/web/packages/WaveletRF. Accessed 22 Dec. 2024.
Previous versions and publish date:
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Complete documentation for WaveletRF
Functions, R codes and Examples using the WaveletRF R package
Some associated functions: WaveletFitting . WaveletFittingRF . 
Some associated R codes: WaveletRF.R .  Full WaveletRF package functions and examples
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