Short term prediction of sales in supermarkets

In this paper artificial neural networks are applied to a short term forecast of the sale of articles in supermarkets. The times series of sales, prices and advertising campaigns are modelled to fit into feedforward multilayer perceptron networks that are trained by the backpropagation algorithm. Several network topologies and training parameters have been compared. For enhancement the backpropagation algorithm has been parallelized in different manners. One batch and two online training algorithms are implemented on parallel systems with both the runtime environments PARIX and PVM. The research leads to a practical forecasting system for supermarkets