  Following the "decomposition-and-ensemble" principle, the empirical mode
decomposition (EMD)-based modeling framework has been widely used as a
promising alternative for nonlinear and nonstationary time series modeling and
prediction. The end effect, which occurs during the sifting process of EMD and
is apt to distort the decomposed sub-series and hurt the modeling process
followed, however, has been ignored in previous studies. Addressing the end
effect issue, this study proposes to incorporate end condition methods into
EMD-based decomposition and ensemble modeling framework for one- and multi-step
ahead time series prediction. Four well-established end condition methods,
Mirror method, Coughlin's method, Slope-based method, and Rato's method, are
selected, and support vector regression (SVR) is employed as the modeling
technique. For the purpose of justification and comparison, well-known NN3
competition data sets are used and four well-established prediction models are
selected as benchmarks. The experimental results demonstrated that significant
improvement can be achieved by the proposed EMD-based SVR models with end
condition methods. The EMD-SBM-SVR model and EMD-Rato-SVR model, in particular,
achieved the best prediction performances in terms of goodness of forecast
measures and equality of accuracy of competing forecasts test.
