Hierarchical sales forecasting using Recurrent Neural Networks

Abstract: Sales forecasting equips businesses with the essential basis for planning future investments, controlling costs, and production. This research is in cooperation with a property development company for the purpose of improving the accuracy of manual sales forecasting. The objective is to investigate the effects of using the underlying factors that affect the individual sales of the company in forecasting the company’s income. One approach uses an aggregation of the estimates of the individual sales to approximate the company’s income. This approach uses the underlying hierarchical factors of the company’s individual sales to forecast future sales, which is known as the bottom-up approach. Another approach, known as the direct approach, uses the history of the company’s income instead. The bottom-up approach estimates the income of the company in the chosen target quarter, Q4 2019, with a percentage error of 33 percent. On the contrary, the direct approach provides an estimate of the company’s income inQ4 2019 with a percentage error of 3 percent. The strength of the bottom-up approach is in providing detailed forecasts of the individual sales of the company. The direct approach, however, is more convenient in learning the overall behavior of the company’s earnings.