Forecasting Non-normal Demand by Support Vector Machines with Ensemble Empirical Mode Decomposition

Abstract

Non-Normal demand is the demand with infrequent demand occurrences or irregular demand sizes, which is very difficult to forecast. In this study, an ensemble empirical mode decomposition (EEMD) based support vector machines (SVMs) learning approach is proposed to forecast demand in these two cases. This approach is under a “decomposition-and-ensemble” principal to decompose the original non-normal demand series into several independent “smooth” and “continuous” 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 non-normal demand series. Two data sets each has four artificial data series 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 , ARIMA and Croston method in terms of RMSE, MAPE, MdRAE and MASE.