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Abstract: It is important for retail enterprises to forecast the sales volume as accurately as possible.By building auto-regressive moving average model (ARMA) can forecast and analyze the retail sales volume. The traditional ARMA model can not describe the unstable and nonlinear characteristics which existing in the sales volume. The Support Vector Regression(SVR) method and Extreme Learning Machine(ELM) method are used to predict and compensate the nonlinear errors of ARMA time series model to enhance the accuracy of prediction. And the paper proposed a novel selective ensemble method to determine the parameter of ARMA.This method based on a genetic optimization algorithm and simplified the process of ARMA. We gathered some data to analyze and compare the performance of ARMA、selective ensembled ARMA、ARMA-SVR and ARMA-ELM. The experimental results show that the accuracy of selective ensembled ARMA model are increased by 23.58% and 41.28% in both stable and unstable series. The combination forecasting model is more in line with the actual data than time series forecasting model. Aslo, ARMA-SVR model’s accuracy of prediction is about one-third higher than ARMA-ELM .