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Regional Economic Development Trend Prediction Method Based on Digital Twins and Time Series Network

Runguo Xu*, Xuehan Yu, Xiaoxue Zhao

School of International Relations, Yonsei University, Seoul, 03722, Korea

* Corresponding Author: Runguo Xu. Email: email

Computers, Materials & Continua 2023, 76(2), 1781-1796.


At present, the interpretation of regional economic development (RED) has changed from a simple evaluation of economic growth to a focus on economic growth and the optimization of economic structure, the improvement of economic relations, and the change of institutional innovation. This article uses the RED trend as the research object and constructs the RED index to conduct the theoretical analysis. Then this paper uses the attention mechanism based on digital twins and the time series network model to verify the actual data. Finally, the regional economy is predicted according to the theoretical model. The specific research work mainly includes the following aspects: 1) This paper introduced the development status of research on time series networks and economic forecasting at home and abroad. 2) This paper introduces the basic principles and structures of long and short-term memory (LSTM) and convolutional neural network (CNN), constructs an improved CNN-LSTM model combined with the attention mechanism, and then constructs a regional economic prediction index system. 3) The best parameters of the model are selected through experiments, and the trained model is used for simulation experiment prediction. The results show that the CNN-LSTM model based on the attention mechanism proposed in this paper has high accuracy in predicting regional economies.


Cite This Article

R. Xu, X. Yu and X. Zhao, "Regional economic development trend prediction method based on digital twins and time series network," Computers, Materials & Continua, vol. 76, no.2, pp. 1781–1796, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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