Neural Network Associative Forecasting of Demand for Goods

This article discusses the applicability of recurrent neural networks with controlled elements to the problem of forecasting market demand for goods on the four month horizon. Two variants of forecasting are considered. In the first variant, time series are used to train the neural network, including the real demand values, as well as pre-order values for 1, 2 and 3 months ahead. In the second variant, there is an iterative forecasting method. It predicts the de-mand for the next month at each step, and the training set is supplemented by the values predicted for the previous months. It is shown that the proposed methods can give a sufficiently high result. At the same time, the second ap-proach demonstrates greater potential.