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

Author

Listed:
  • Oleg Kryzhanovskiy

    (Bank of Russia; Tyumen State University)

  • Anastasia Mogilat

    (Bank of Russia)

  • Zhanna Shuvalova

    (Bank of Russia)

  • Dmitry Gvozdev

    (HSE University)

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.

Suggested Citation

  • Oleg Kryzhanovskiy & Anastasia Mogilat & Zhanna Shuvalova & Dmitry Gvozdev, 2025. "Using LSTM Neural Networks for Nowcasting and Forecasting GVA of Industrial Sectors," Russian Journal of Money and Finance, Bank of Russia, vol. 84(1), pages 93-104, March.
    • Handle: RePEc:bkr:journl:v:84:y:2025:i:1:p:93-104
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    References listed on IDEAS

    as
    1. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    2. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    3. Evgeny Pavlov, 2020. "Forecasting Inflation in Russia Using Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 79(1), pages 57-73, March.
    4. Porshakov, A. & Ponomarenko, A. & Sinyakov, A., 2016. "Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model," Journal of the New Economic Association, New Economic Association, vol. 30(2), pages 60-76.
    5. Urmat Dzhunkeev, 2024. "Forecasting Inflation in Russia Using Gradient Boosting and Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 83(1), pages 53-76, March.
    6. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General

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