Abstract
Elastic net (ELNET) regression is a hybrid statistical technique used for regularizing and selecting necessary predictor variables that have a strong effect on the response variable and deal with multicollinearity problem when it exists between the predictor variables. The empirical mode decomposition (EMD) algorithm is used to decompose the nonstationary and nonlinear dataset into a finite set of orthogonal intrinsic mode function components and one residual component. This study mainly aims to apply the proposed ELNET-EMD method to determine the effect of the decomposition components of multivariate time-series predictors on the response variable and tackle the multicollinearity between the decomposition components to enhance the prediction accuracy for building a fitting model. A numerical experiment and a real data application are applied. Results show that the proposed ELNET-EMD method outperforms other existing methods by capable of identifying the decomposition components that have the most significance on the response variable despite the high correlation between the decomposition components and by improving the prediction accuracy.
Highlights
Several studies such as medicine, and economics interested in using time series datasets, where these datasets are often non-stationary and non-linear simultaneously
This finding indicates that a high correlation exists among the decomposition components of the MYR, JAP, and CHN variables, which subsequently indicates that multicollinearity exists
For the minimum of the MSE (minM) rule, twelve components from all decomposition components are selected into the final model, while for the 1se rule, only the C1,1 and C3,1components have the strongest effect on the response variable and that is why it is entered into the final model
Summary
Several studies such as medicine, and economics interested in using time series datasets, where these datasets are often non-stationary and non-linear simultaneously. Huang et al (1998) proposed the empirical mode decomposition (EMD) method, which aims to decompose non-stationary and non-linear data with keeping the time domain. These components can be used as new predictor variables to predict the response variable and improve the prediction accuracy of regression analysis. Multicollinearity defined as a relationship between the predictor variables, which increases the variance. This results in a wrong sign of coefficients and misleads the selection of a fitting model (Jadhav et al, 2014)
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