It has been shown that feedforward multilayer perceptron networks can learn to approximate the time series of sales in supermarkets. For a special group of articles neural networks can be trained to forecast the future demand on the basis of the past data together with external information like changing prices and advertising.
For the future the input vectors should be improved: especially season and holiday information have to be given to the net; the value of changing prices can be modelled quantitatively.
One important aim will be the reduction of input neurons. By correlation analysis some of the hundreds of single time series should be merged or denied. This will lead to smaller nets with shorter training times.