2019, Vol. 4, Issue 6, Part A
A hybrid statistical empirical mode decomposition with neural network in time series forecastingAuthor(s):
Dr. Mohammed Abou Elfettouh Ghazal, Dr. Asaad Ahmed Gad Elrab and Wafa Hamed Abd AllahAbstract:
Scope Extending of Empirical Mode Decomposition called Statistical Empirical Mode Decomposition (SEMD), recently proposed by Kim et al
. (2012) invented a new data analysis technique for nonlinear and non-stationary time series. By breaks a time series into a small number of independent and concretely implicational intrinsic modes functions based on scale separation, SEMD explains the generation of time series data from a novel perspective. This study illustrates a statistical empirical mode decomposition based on neural network learning paradigm (SEMD-NN) for forecasting Egypt stock market. By the criteria of some statistic loss functions, SEMD-NN outperforms Holt-winters family model, empirical mode decomposition based on neural network (EMD-NN) and ensample empirical mode decomposition and neural network (EEMD-NN) in improving forecast accuracy.Pages: 45-47 | Views: 789 | Downloads: 19Download Full Article: Click Here
How to cite this article:
Dr. Mohammed Abou Elfettouh Ghazal, Dr. Asaad Ahmed Gad Elrab, Wafa Hamed Abd Allah. A hybrid statistical empirical mode decomposition with neural network in time series forecasting. Int J Stat Appl Math 2019;4(6):45-47.