In this article, the quantity of grapes sold in one fruit shop of an#N#                            interlocking fruit supermarket is forecasted by the method of support#N#                            vector machine (SVM) based on deficient data. Since SVMs have a lot#N#                            advantages such as great generalization performance and guarantying#N#                            global minimum for given training data, it is believed that support#N#                            vector regression will perform well for forecasting sales of grapes. In#N#                            order to improve forecasting precision (FP), this article quantifies the#N#                            factors affecting the sales forecast of grapes such as weather and#N#                            weekend or weekday, results are suitable for real situations. In this#N#                            article, we apply e-SVR and#N#                            LS-SVR to forecast sales of three varieties of grapes. Moreover, the#N#                            artificial neural network (ANN) and decision tree (DT) are used as#N#                            contrast and numerical experiments show that forecasting systems with#N#                            SVMs is better than ANN and DT to forecast the daily sales of grapes#N#                            overall.
['Qian Wen', 'Weisong Mu', 'Li Sun', 'Su Hua', 'Zhijian Zhou']
Daily Sales Forecasting for Grapes by Support Vector Machine