Many researchers documented that the stock market data are nonstationary and nonlinear time series data. In this study, we use EMD-HW bagging method for nonstationary and nonlinear time series forecasting. The EMD-HW bagging method is based on the empirical mode decomposition (EMD), the moving block bootstrap and the Holt-Winter. The stock market time series of six countries are used to compare EMD-HW bagging method. This comparison is based on five forecasting error measurements. The comparison shows that the forecasting results of EMD-HW bagging are more accurate than the forecasting results of the fourteen selected methods.
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.
Recently, forecasting time series has attracted considerable attention in the field of analyzing financial time series data, specifically within the stock market index. Moreover, stock market forecasting is a challenging area of financial time-series forecasting. In this study, a hybrid methodology between Empirical Mode Decomposition with the Moving Average Model (EMD-MA) is used to improve forecasting performances in financial time series. The strength of this EMD-MA lies in its ability to forecast non-stationary and non-linear time series without a need to use any
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