Daily sales forecasting for grapes by support vector machine

Abstract

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