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In this study, an ensemble empirical mode decomposition (EEMD) based support vector machines (SVMs) learning approach is proposed for erratic demand forecast. This approach is under a "decomposition-and-ensemble" principal to decompose the original erratic demand series into several independent "smooth" subseries including a small number of intrinsic mode functions (IMFs) and a residue by EEMD technique. Then SVMs are used to model each of the subseries so as to achieve more accurate forecast respectively. Finally, the forecasts of all subseries are aggregated by a SVMs model to formulate an ensemble forecast for the erratic demand series. Four artificial data sets were used to test the effectiveness of the proposed approach. Empirical results demonstrate that the proposed ensemble learning approach outperforms the other forecasting methods such as SVMs and AREVIA in terms of RMSE, MAPE, MdRAE and GMRAE.