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

Oleg Kryzhanovskiy (), Anastasia Mogilat (), Zhanna Shuvalova () and Dmitry Gvozdev ()
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Oleg Kryzhanovskiy: Bank of Russia; Tyumen State University
Anastasia Mogilat: Bank of Russia
Zhanna Shuvalova: Bank of Russia
Dmitry Gvozdev: HSE University

Russian Journal of Money and Finance, 2025, vol. 84, issue 1, 93-104

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.

Keywords: GDP; GVA; neural networks; long short-term memory network; nowcasting; forecasting (search for similar items in EconPapers)
JEL-codes: C45 C53 C82 E17 L60 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:bkr:journl:v:84:y:2025:i:1:p:93-104

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