Using LSTM Neural Networks for Nowcasting and Forecasting GVA of Industrial Sectors

Abstract

This paper evaluates the potential application of long short-term memory (LSTM) neural networks for economic forecasting. We compare the accuracy of short-term forecasts of the gross value added of industrial sectors obtained using an LSTM model against several benchmarks, such as a random walk model, an autoregressive integrated moving average model, and an approximate dynamic factor model. Compared to the other models, the LSTM model demonstrates a lower mean absolute forecast error in 16 out of 18 cases and a lower root mean square error in 13 out of 18 cases.