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CEEMDANML  

CEEMDAN Decomposition Based Hybrid Machine Learning Models
View on CRAN: Click here


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

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

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



Attach the package and use:
library("CEEMDANML")
Maintained by
Mr. Sandip Garai
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-04-07
Latest Update: 2023-04-07
Description:
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) .
How to cite:
Mr. Sandip Garai (2023). CEEMDANML: CEEMDAN Decomposition Based Hybrid Machine Learning Models. R package version 0.1.0, https://cran.r-project.org/web/packages/CEEMDANML. Accessed 21 Nov. 2024.
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