Computer modeling of the gross regional product dynamics: a comparative analysis of neural network models

Vavilova D.D., Ketova K.V., Zerari R.
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Analysis of regional economic indicators plays a crucial role in management and development planning, with Gross Regional Product (GRP) serving as one of the key indicators of economic activity. The application of artificial intelligence, including neural network technologies, enables significant improvements in the accuracy and reliability of forecasts of economic processes. This study compares three neural network algorithm models for predicting the GRP of a typical region of the Russian Federation — the Udmurt Republic — based on time series data from 2000 to 2023. The selected models include a neural network with the Bat Algorithm (BA-LSTM), a neural network model based on backpropagation error optimized with a Genetic Algorithm (GA-BPNN), and a neural network model of Elman optimized using the Particle Swarm Optimization algorithm (PSO-Elman). The research involved stages of neural network modeling such as data preprocessing, training model, and comparative analysis based on accuracy and forecast quality metrics. This approach allows for evaluating the advantages and limitations of each model in the context of GRP forecasting, as well as identifying the most promising directions for further research. The utilization of modern neural network methods opens new opportunities for automating regional economic analysis and improving the quality of forecast assessments, which is especially relevant when data are limited and for rapid decision-making. The study uses factors such as the amount of production capital, the average annual number of labor resources, the share of high-tech and knowledge-intensive industries in GRP, and an inflation indicator as input data for predicting GRP. The high accuracy of the predictions achieved by including these factors in the neural network models confirms the strong correlation between these factors and GRP. The results demonstrate the exceptional accuracy of the BA-LSTM neural network model on validation data: the coefficient of determination was 0.82, and the mean absolute percentage error was 4.19%. The high performance and reliability of this model confirm its capacity to predict effectively the dynamics of the GRP. During the forecast period up to 2030, the Udmurt Republic is expected to experience an annual increase in Gross Regional Product (GRP) of +4.6% in current prices or +2.5% in comparable 2023 prices. By 2030, the GRP is projected to reach 1264.5 billion rubles.

Keywords: gross regional product (GRP), neural network models, BA-LSTM neural network, GA-BPNN model, PSO-Elman neural network
Citation in English: Vavilova D.D., Ketova K.V., Zerari R. Computer modeling of the gross regional product dynamics: a comparative analysis of neural network models // Computer Research and Modeling, 2025, vol. 17, no. 6, pp. 1219-1236
Citation in English: Vavilova D.D., Ketova K.V., Zerari R. Computer modeling of the gross regional product dynamics: a comparative analysis of neural network models // Computer Research and Modeling, 2025, vol. 17, no. 6, pp. 1219-1236
DOI: 10.20537/2076-7633-2025-17-6-1219-1236
URL: http://crm-en.ics.org.ru/journal/article/3675/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

Copyright © 2025 Vavilova D.D., Ketova K.V., Zerari R.

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