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decompML  

Decomposition Based Machine Learning Model
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("decompML", "0.1.1")



Attach the package and use:
library("decompML")
Maintained by
Kapil Choudhary
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-02-18
Latest Update: 2025-02-18
Description:
The hybrid model is a highly effective forecasting approach that integrates decomposition techniques with machine learning to enhance time series prediction accuracy. Each decomposition technique breaks down a time series into multiple intrinsic mode functions (IMFs), which are then individually modeled and forecasted using machine learning algorithms. The final forecast is obtained by aggregating the predictions of all IMFs, producing an ensemble output for the time series. The performance of the developed models is evaluated using international monthly maize price data, assessed through metrics such as root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). For method details see Choudhary, K. et al. (2023). <https://ssca.org.in/media/14_SA44052022_R3_SA_21032023_Girish_Jha_FINAL_Finally.pdf>.
How to cite:
Kapil Choudhary (2025). decompML: Decomposition Based Machine Learning Model. R package version 0.1.1, https://cran.r-project.org/web/packages/decompML. Accessed 12 Sep. 2026.
Previous versions and publish date:
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Complete documentation for decompML
Functions, R codes and Examples using the decompML R package
Full decompML package functions and examples
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