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.

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The empirical mode decomposition (EMD) method is used to decompose the non-stationary and nonlinear signal into a finite set of orthogonal non-overlapping time scale components that include several intrinsic mode function components and one residual component. Elastic net (ELN) regression is a statistical penalized method used to address multicollinearity among predictor variables and identify the necessary variables that have the most effect on the response variable. This study proposed the use of the ELN method based on the EMD algorithm to identify the decomposition components of multivariate predictor variables with the most effect on the response variable under multicollinearity problems. The results of the numerical experiments and real data confirmed that the EMD-ELN method is highly capable of identifying the decomposition components with the presence or absence of multicollinearity among the components. The proposed method also achieved the best estimation and reached the optimal balance between the variance and bias. The EMD-ELN method also improved the accuracy of regression modeling compared with the traditional regression models.

According on the simulation findings, the suggested technique works well in model fitting. Real-data applications indicate that including some previous information as parameter restrictions results in models with higher prediction accuracy.Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

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