Prediction of macroeconomic indicators based on recurrent neural network

The article is devoted to the study of the use of recurrent neural networks (RNN) to predict macroeconomic indicators such as GDP, inflation and un employment. The basic principles of RNN operation, their advantages over traditional methods of time series analysis, as well as practical aspects of model development and implementation are considered. Particular attention is paid to assessing the accuracy of forecasts and identifying factors affecting the effectiveness of the model. Based on the analysis, recommendations are made on the use of RNN to solve macroeconomic forecasting problems.