A comparison of sales forecasting methods for a feed company: A case study

Abstract

Due to global warming in recent years, using natural resources in an effective way has become more and more important to our world. Decreasing natural resources are pushing agriculture and food chains to adopt more efficient management strategies. The first condition for a successful management is to make plans based on accurate and reliable forecasts. In this study, using real-world data, forecasting models are compared for the products of a feed company which is the first chain of agriculture and food chain systems. The traditional statistical time series methods are compared to two popular and effective computational intelligence techniques, i.e. artificial neural networks and support vector regression. The accuracy of the forecasts is calculated by three different error measures, i.e., the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the mean squared error (MSE). The results show that support vector machines produces significantly better results comparing to both time series methods and artificial neural networks.