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
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Acceso en línea:https://doaj.org/article/dde841e1909545978bbd2eb8f0395e61
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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
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