Application of artificial neural networks in sales forecasting
D.H.F. Yip, Evor L. Hines, W.W.H. Yu · Proceedings of International Conference on Neural Networks (ICNN'97) · 2002
The aim of the work presented in this paper is to forecast sales volumes as accurately as possible and as far into the future as possible. The choice of network topology was Silva's adaptive backpropagation algorithm and the network architectures were selected by genetic algorithms (GAs). The networks were trained to forecast from 1 month to 6 months in advance and the performance of the network was tested after training. The test results of artificial neural networks (ANNs) are compared with the time series smoothing methods of forecasting using several measures of accuracy. The outcome of the comparison proved that the ANNs generally perform better than the time series smoothing methods of forecasting. Further recommendations resulting from this paper are presented.