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A Regional Industrial Economic Forecasting Model Based on a Deep Convolutional Neural Network and Big Data

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  • Shouheng Tuo

    (School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
    Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an 710121, China
    Xi’an Key Laboratory of Big Data and Intelligent Computing, Xi’an 710121, China)

  • Tianrui Chen

    (School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China)

  • Hong He

    (College of Economics and Management, Xi’an University of Posts & Telecommunications, Xi’an 710121, China)

  • Zengyu Feng

    (School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China)

  • Yanling Zhu

    (School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China)

  • Fan Liu

    (School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China)

  • Chao Li

    (School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China)

Abstract

To accurately predict the economic development of each industry in different types of regions, a deep convolutional neural network model was designed for predicting the annual GDP; GDP growth index; and primary, secondary and tertiary industry growth values of each. In the model, raw industrial data are preprocessed by a normalization operation and subsequently transformed by the BoxCox method to approach the normal distribution. Panel data of consecutive years are constructed and used as input to the deep convolutional neural network, and industrial data of year t + 1 are used as the output of the network. Simulation experiments were conducted to analyze 23 years of industrial economic data from 31 provinces, municipalities, and autonomous regions in China. The experimental results show that R-squared value is larger than 0.91 for all 31 provinces and root mean squared log errors (RMSLE) of all regions are less than 0.1, which demonstrate that the proposed method achieves high prediction accuracy with generalization capability and can accurately predict the economic growth trends of different types of regions.

Suggested Citation

  • Shouheng Tuo & Tianrui Chen & Hong He & Zengyu Feng & Yanling Zhu & Fan Liu & Chao Li, 2021. "A Regional Industrial Economic Forecasting Model Based on a Deep Convolutional Neural Network and Big Data," Sustainability, MDPI, vol. 13(22), pages 1-11, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:22:p:12789-:d:682814
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    References listed on IDEAS

    as
    1. Lyu, Yifei & Nie, Jun & Yang, Shu-Kuei X., 2021. "Forecasting US economic growth in downturns using cross-country data," Economics Letters, Elsevier, vol. 198(C).
    2. Shouheng Tuo & Hong He, 2021. "A Study of Multiregional Economic Correlation Analysis Based on Big Data—Taking the Regional Economy of Cities in Shaanxi Province, China, as an Example," Sustainability, MDPI, vol. 13(9), pages 1-13, May.
    3. Claveria, Oscar & Monte, Enric & Torra, Salvador, 2020. "Economic forecasting with evolved confidence indicators," Economic Modelling, Elsevier, vol. 93(C), pages 576-585.
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    Cited by:

    1. Aleksey I. Shinkevich & Irina G. Ershova & Farida F. Galimulina, 2022. "Forecasting the Efficiency of Innovative Industrial Systems Based on Neural Networks," Mathematics, MDPI, vol. 11(1), pages 1-25, December.
    2. Qingwen Li & Guangxi Yan & Chengming Yu, 2022. "A Novel Multi-Factor Three-Step Feature Selection and Deep Learning Framework for Regional GDP Prediction: Evidence from China," Sustainability, MDPI, vol. 14(8), pages 1-21, April.

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