Sales forecasting using neural networks

Abstract

Neural networks trained with the backpropagation algorithm are applied to predict the future values of time series that consist of the weekly demand on items in a supermarket. The influencing indicators of prices, advertising campaigns and holidays are taken into consideration. The design and implementation of a neural network forecasting system is described that has been developed as a prototype for the headquarters of a German supermarket company to support the management in the process of determining the expected sale figures. The performance of the networks is evaluated by comparing them to two prediction techniques used in the supermarket now. The comparison shows that neural nets outperform the conventional techniques with regard to the prediction quality.

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