Industrial Efficiency Algorithm Based on Spatio-Temporal-Data-Driven

Stochastic frontier model is an important and effective method to calculate industry efficiency. However, when dealing with temporal and spatial data from the industry, it is difficult to accurately calculate the industrial production efficiency due to the influence of spatial correlation and time l...

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Autores principales:	Hongqu Lv, Wensi Cheng
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology T Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/dde841e1909545978bbd2eb8f0395e61
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id	oai:doaj.org-article:dde841e1909545978bbd2eb8f0395e61
record_format	dspace
spelling	oai:doaj.org-article:dde841e1909545978bbd2eb8f0395e612021-11-22T01:09:32ZIndustrial Efficiency Algorithm Based on Spatio-Temporal-Data-Driven1530-867710.1155/2021/7439744https://doaj.org/article/dde841e1909545978bbd2eb8f0395e612021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7439744https://doaj.org/toc/1530-8677Stochastic frontier model is an important and effective method to calculate industry efficiency. However, when dealing with temporal and spatial data from the industry, it is difficult to accurately calculate the industrial production efficiency due to the influence of spatial correlation and time lag effect. If the traditional spatial statistical method is used, the setting method of spatial weight matrix is often questioned. To solve this series of problems, one possible idea is to design a spatial data mining process based on stochastic frontier analysis. Firstly, the stochastic frontier model should be improved to analyze spatio-temporal data. In order to accurately measure the technical efficiency in the case of dual correlation between time and space, a more effective spatio-temporal stochastic frontier model method is proposed. Meanwhile, based on the idea of generalized moment estimation, an estimation method of spatiotemporal stochastic frontier model is designed, and the consistency of estimators is proved. In order to ensure that the most appropriate spatial weight matrix can be selected in the process of model construction, the K-fold crossvalidation method is adopted to evaluate the prediction effect under the data-driven idea. This set of spatio-temporal data mining methods will be used to measure the technical efficiency of high-tech industries in various provinces of China.Hongqu LvWensi ChengHindawi-WileyarticleTechnologyTTelecommunicationTK5101-6720ENWireless Communications and Mobile Computing, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Telecommunication TK5101-6720
spellingShingle	Technology T Telecommunication TK5101-6720 Hongqu Lv Wensi Cheng Industrial Efficiency Algorithm Based on Spatio-Temporal-Data-Driven
description	Stochastic frontier model is an important and effective method to calculate industry efficiency. However, when dealing with temporal and spatial data from the industry, it is difficult to accurately calculate the industrial production efficiency due to the influence of spatial correlation and time lag effect. If the traditional spatial statistical method is used, the setting method of spatial weight matrix is often questioned. To solve this series of problems, one possible idea is to design a spatial data mining process based on stochastic frontier analysis. Firstly, the stochastic frontier model should be improved to analyze spatio-temporal data. In order to accurately measure the technical efficiency in the case of dual correlation between time and space, a more effective spatio-temporal stochastic frontier model method is proposed. Meanwhile, based on the idea of generalized moment estimation, an estimation method of spatiotemporal stochastic frontier model is designed, and the consistency of estimators is proved. In order to ensure that the most appropriate spatial weight matrix can be selected in the process of model construction, the K-fold crossvalidation method is adopted to evaluate the prediction effect under the data-driven idea. This set of spatio-temporal data mining methods will be used to measure the technical efficiency of high-tech industries in various provinces of China.
format	article
author	Hongqu Lv Wensi Cheng
author_facet	Hongqu Lv Wensi Cheng
author_sort	Hongqu Lv
title	Industrial Efficiency Algorithm Based on Spatio-Temporal-Data-Driven
title_short	Industrial Efficiency Algorithm Based on Spatio-Temporal-Data-Driven
title_full	Industrial Efficiency Algorithm Based on Spatio-Temporal-Data-Driven
title_fullStr	Industrial Efficiency Algorithm Based on Spatio-Temporal-Data-Driven
title_full_unstemmed	Industrial Efficiency Algorithm Based on Spatio-Temporal-Data-Driven
title_sort	industrial efficiency algorithm based on spatio-temporal-data-driven
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/dde841e1909545978bbd2eb8f0395e61
work_keys_str_mv	AT hongqulv industrialefficiencyalgorithmbasedonspatiotemporaldatadriven AT wensicheng industrialefficiencyalgorithmbasedonspatiotemporaldatadriven
_version_	1718418431416467456

Industrial Efficiency Algorithm Based on Spatio-Temporal-Data-Driven

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