Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition - Awajan, Ahmad Mohammad Al-Abd (2018)
The stock market indices are typically non-linear and non-stationary with high heteroscedasticity data, which affect the accuracy and validity of the results of traditional forecasting methods. Therefore, this study focuses on decomposition method to solve the problem of non-linearity and non-stationarity in data with high heteroscedasticity behavior to improve the accuracy of stock market forecasting. Recently, Empirical mode decomposition (EMD) method has been introduced as an effective technique for overcoming the non-linearity and non-stationarity in time series data. EMD presents several characteristics that other decomposition methods do not have.