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SVMD  

Spearman Variational Mode Decomposition
View on CRAN: Click here


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

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

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



Attach the package and use:
library("SVMD")
Maintained by
Dr. Himadri Shekhar Roy
[Scholar Profile | Author Map]
First Published: 2024-09-16
Latest Update: 2024-09-16
Description:
In practice, it is difficult to determine the number of decomposition modes, K, for Variational Mode Decomposition (VMD). To overcome this issue, this study offers Spearman Variational Mode Decomposition (SVMD), a method that uses the Spearman correlation coefficient to calculate the ideal mode number. Unlike the Pearson correlation coefficient, which only returns a perfect value when X and Y are linearly connected, the Spearman correlation can be calculated without knowing the probability distributions of X and Y. The Spearman correlation coefficient, also called Spearman's rank correlation coefficient, is a subset of a wider correlation coefficient. As VMD decomposes a signal, the Spearman correlation coefficient between the reconstructed and original sequences rises as the mode number K increases. Once the signal has been fully decomposed, subsequent increases in K cause the correlation to gradually level off. When the correlation reaches a specific level, VMD is said to have adequately decomposed the signal. Numerous experiments revealed that a threshold of 0.997 produces the best denoising effect, so the threshold is set at 0.997. This package has been developed using concept of Yang et al. (2021)<doi:10.1016/j.aej.2021.01.055>.
How to cite:
Dr. Himadri Shekhar Roy (2024). SVMD: Spearman Variational Mode Decomposition. R package version 0.1.0, https://cran.r-project.org/web/packages/SVMD. Accessed 17 Feb. 2025.
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Functions, R codes and Examples using the SVMD R package
Full SVMD package functions and examples
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