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

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.