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Forecasting Stock Market Using hybrid EMD-EXP

Ahmad M. Awajan, Mohd Tahir Ismail, S. AL Wadi, Abobaker.M. Jaber, Lubna M. Hamzalouh


Forecasting time series recently has attracted considerable attention in the field of analyzing financial time series data specifically stock market index. This considerable attention confined itself in the need of transparent change in the governmental policies whether attracting foreign investment or and economical advancements. In this study, a hybrid methodology between Empirical Mode Decomposition with exponential smoothing method
(EMD-EXP) is applied to improve forecasting performances in financial time series. The strength of this EMD-EXP lies in its ability to predict non-stationary and non-linear time series without need to use any transformation method or differencing of time series data. The daily stock market data of 4 countries are used to show the forecasting performance of the EMD-EXP. Based on the five forecast accuracy functions, the results show that the
EMD-EXP forecasting performance is superior to seven traditional forecasting methods.


forecast time series, empirical mode decomposition (EMD), exponential smoothing forecasting (EXP), intrinsic mode function (IMF), seasonal-trend decomposition (STL).

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