International Journal of Statistics and Applied Mathematics
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International Journal of Statistics and Applied Mathematics

2019, Vol. 4, Issue 6, Part A

A hybrid statistical empirical mode decomposition with neural network in time series forecasting


Author(s): Dr. Mohammed Abou Elfettouh Ghazal, Dr. Asaad Ahmed Gad Elrab and Wafa Hamed Abd Allah

Abstract: 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: 131 | Downloads: 14

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How to cite this article:
Dr. Mohammed Abou Elfettouh Ghazal, Dr. Asaad Ahmed Gad Elrab and Wafa Hamed Abd Allah. A hybrid statistical empirical mode decomposition with neural network in time series forecasting. International Journal of Statistics and Applied Mathematics. 2019; 4(6): 45-47.
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