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

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...

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Autores principales:	Shouheng Tuo, Tianrui Chen, Hong He, Zengyu Feng, Yanling Zhu, Fan Liu, Chao Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep convolutional neural network regional economy industrial economic big data Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/ff802ee080954ffe94ed530d437cfdbd
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id	oai:doaj.org-article:ff802ee080954ffe94ed530d437cfdbd
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spelling	oai:doaj.org-article:ff802ee080954ffe94ed530d437cfdbd2021-11-25T19:04:22ZA Regional Industrial Economic Forecasting Model Based on a Deep Convolutional Neural Network and Big Data10.3390/su1322127892071-1050https://doaj.org/article/ff802ee080954ffe94ed530d437cfdbd2021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12789https://doaj.org/toc/2071-1050To 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.Shouheng TuoTianrui ChenHong HeZengyu FengYanling ZhuFan LiuChao LiMDPI AGarticledeep convolutional neural networkregional economyindustrial economic big dataEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12789, p 12789 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep convolutional neural network regional economy industrial economic big data Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	deep convolutional neural network regional economy industrial economic big data Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Shouheng Tuo Tianrui Chen Hong He Zengyu Feng Yanling Zhu Fan Liu Chao Li A Regional Industrial Economic Forecasting Model Based on a Deep Convolutional Neural Network and Big Data
description	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.
format	article
author	Shouheng Tuo Tianrui Chen Hong He Zengyu Feng Yanling Zhu Fan Liu Chao Li
author_facet	Shouheng Tuo Tianrui Chen Hong He Zengyu Feng Yanling Zhu Fan Liu Chao Li
author_sort	Shouheng Tuo
title	A Regional Industrial Economic Forecasting Model Based on a Deep Convolutional Neural Network and Big Data
title_short	A Regional Industrial Economic Forecasting Model Based on a Deep Convolutional Neural Network and Big Data
title_full	A Regional Industrial Economic Forecasting Model Based on a Deep Convolutional Neural Network and Big Data
title_fullStr	A Regional Industrial Economic Forecasting Model Based on a Deep Convolutional Neural Network and Big Data
title_full_unstemmed	A Regional Industrial Economic Forecasting Model Based on a Deep Convolutional Neural Network and Big Data
title_sort	regional industrial economic forecasting model based on a deep convolutional neural network and big data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ff802ee080954ffe94ed530d437cfdbd
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A Regional Industrial Economic Forecasting Model Based on a Deep Convolutional Neural Network and Big Data

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