Forecasting the retail sales of China’s catering industry using support vector machines

The forecast of Chinapsilas catering retail sales was studied in this paper. The seasonal impact was considered in the forecasting. The retail sales were predicted using the seasonal auto-regressive integrated moving average (ARIMA) model. As a comparison, the retail sales also were predicted by using support vector machine (SVM), a supervised learning method. By evaluating the prediction errors, we found that the SVM method is obviously superior to the seasonal ARIMA method regardless of the long-term forecasting or the short-term forecasting. It shows that, for a time series included the seasonal factors, the SVM method can provide a fairly good predicting result and the model based SVM is better the ability of generalization than the traditional model.