VMDSVR {VMDML}

Variational Mode Decomposition Based Support Vector Regression Model

Description

The VMDSVR function helps to fit the Variational Mode Decomposition based Support Vector Regression Model. It will also provide you with accuracy measures along with an option to select the proportion of training and testing data sets. Users can choose among the available choices of kernel and types of regresion model for fitting the Support Vector Regression model. In this package we have modelled the dependency of the study variable assuming first order autocorrelation. This package will help the researchers working in the area of hybrid machine learning models.

Usage

VMDSVR(data,k,alpha,tau,K,DC,init,tol, ker.funct="",svm.type="")

Arguments

data

input univariate time series data.

k

partition value for spliting the data set into training and testing.

alpha

a numeric value specifying the balancing parameter of the data-fidelity constraint.

tau

a numeric value specifying the time-step of the dual ascent ( pick 0 for noiseslack ).

K

a numeric value specifying the number of modes to be recovered.

DC

a boolean. If true the first mode is put and kept at DC (0-freq).

init

a numeric value. This parameter differs depending on the input data parameter (1-dimensional and 2-dimensional)

tol

a numeric value specifying the tolerance of convergence criterion (typically this parameter is around 1e-6 for the 1-dimensional and 1e-7 for the 2-dimensional data).

ker.funct

The available choices of kernel functions like radial basis, linear, polynomial and sigmoidfor fitting Support Vector Regression. By default radial basis function works.

svm.type

SVM can be used as a regression machine. User can apply eps-regression or nu-regression. By default the VMDSVR uses eps-regression.

Details

Variational mode decomposition (VMD) is one of the latest signal decomposition techniques, similar to EMD, first proposed by Dragomiretskiy and Zosso (2014). This is a an entirely non-recursive variational mode decomposition model,where the modes are extracted concurrently. The algorithm generates an ensemble of modes and their respective center frequencies, such that the modes collectively reproduce the input signal. Further Support Vector Regression (SVR) model applied to each decomposed items to forecast them. Finally all forecasted values are aggregated to produce final forecast value (Das et al., 2019, 2020, 2022).

Value

Total_No_IMF

Total number of IMFs after decomposition by VMD method.

Prediction_Accuracy_VMDSVR

List of performance measures of the fitted VMDSVR model.

Final_Prediction_VMDSVR

Final forecasted value of the VMD based SVR model. It is obtained by combining the forecasted value of all individual IMF.

Author(s)

Pankaj Das, Girish Kumar Jha, Tauqueer Ahmad and Achal Lama

References

Dragomiretskiy, K. and Zosso, D.(2014). Variational Mode Decomposition. IEEE Transactions on Signal Processing, 62(3):531-544. (doi: 10.1109/TSP.2013.2288675).

Das,P., Jha,G. K., Lama, A., Parsad, R. and Mishra, D. (2020). Empirical Mode Decomposition based Support Vector Regression for Agricultural Price Forecasting. Indian Journal of Extension Education, 56(2): 7-12. (http://krishi.icar.gov.in/jspui/handle/123456789/44138).

Das, P. Jha, G. K. and Lama, A. (2023). Empirical Mode Decomposition Based Ensemble Hybrid Machine Learning Models for Agricultural Commodity Price Forecasting. Statistics and Applications, 21(1),99-112.(http://krishi.icar.gov.in/jspui/handle/123456789/77772).

Das, P., Jha, G. K., Lama, A. and Bharti (2022). EMD-SVR Hybrid Machine Learning Model and its Application in Agricultural Price Forecasting. Bhartiya Krishi Anusandhan Patrika. (DOI: 10.18805/BKAP385)

Das, P. (2019). Study On Machine Learning Techniques Based Hybrid Model for Forecasting in Agriculture. Published Ph.D. Thesis.

Choudhury, K., Jha, G. K., Das, P. and Chaturvedi, K. K. (2019). Forecasting Potato Price using Ensemble Artificial Neural Networks. Indian Journal of Extension Education, 55(1):71-77. (http://krishi.icar.gov.in/jspui/handle/123456789/44873).

See Also

EMDSVRhybrid, EEMDSVR, VMD, VMDecomp, VMDSVR

Examples

set.seed(6)
data3=rnorm(300,6.6,.36)
alpha = 2000
tau = 0
K= 3
DC = FALSE
init = 1
tol = 1e-6
VMDSVR(data3,.8,alpha,tau,K,DC,init,tol,"radial","nu-regression")