Demand forecasting by the neural network with discrete Fourier transform

This paper proposes a new demand forecasting method using a neural network and Fourier transform. In this method, time series data of sales results considered as a combination of frequency are transformed into several frequency data. They are identified from objective indexes that consist of product properties or economic indicators and so forth. This method is efficient for demand forecasting aimed at new products that have no historical data.