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

Shouheng Tuo, Tianrui Chen, Hong He, Zengyu Feng, Yanling Zhu, Fan Liu and Chao Li
Additional contact information
Shouheng Tuo: School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, 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

Sustainability, 2021, vol. 13, issue 22, 1-11

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.

Keywords: deep convolutional neural network; regional economy; industrial economic big data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/22/12789/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/22/12789/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:22:p:12789-:d:682814

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().