1. A method for forecasting demand for a product based on sales results of the product, comprising: setting plural models as a neural network; identifying sales results of a first period; inputting the identified sales results of the first period to each of the models to make the neural network of each model learn from inputs and produce data as close as possible to sales results of a second period following the first period: storing a forecast demand value of a predetermined time outputted by each of the neural networks; selecting a model from the learned neural networks which has a forecast demand value closest to the sales results of the predetermined time; and inputting latest sales results identified by the learned neural network corresponding to the selected model to forecast demand.