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Agricultural Economic Risk Forecast Based on Data Mining Technology

In order to improve the effect of agricultural economic risk forecast, this paper studies the agricultural economic risk forecast combined with data mining technology and builds an intelligent agricultural economic risk forecast system. Moreover, this paper employs a dynamic factor model to estimate...

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Detalles Bibliográficos
Autores principales: Wang, Lei, Tan, Hongwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9068313/
https://www.ncbi.nlm.nih.gov/pubmed/35528359
http://dx.doi.org/10.1155/2022/3684736
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author Wang, Lei
Tan, Hongwei
author_facet Wang, Lei
Tan, Hongwei
author_sort Wang, Lei
collection PubMed
description In order to improve the effect of agricultural economic risk forecast, this paper studies the agricultural economic risk forecast combined with data mining technology and builds an intelligent agricultural economic risk forecast system. Moreover, this paper employs a dynamic factor model to estimate common factors that drive changes in target topics. In order to construct a sentiment index that can reflect the overall operating situation of the macroeconomy, this paper improves the agricultural economic risk mining algorithm and standardizes the sentiment value corresponding to the target theme. In addition, this article analyzes the sentiment changes of its individual topics one by one in combination with the specific economic environment. The simulation study shows that the agricultural economic risk forecast system based on data mining technology proposed in this paper has a good effect.
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spelling pubmed-90683132022-05-05 Agricultural Economic Risk Forecast Based on Data Mining Technology Wang, Lei Tan, Hongwei Comput Intell Neurosci Research Article In order to improve the effect of agricultural economic risk forecast, this paper studies the agricultural economic risk forecast combined with data mining technology and builds an intelligent agricultural economic risk forecast system. Moreover, this paper employs a dynamic factor model to estimate common factors that drive changes in target topics. In order to construct a sentiment index that can reflect the overall operating situation of the macroeconomy, this paper improves the agricultural economic risk mining algorithm and standardizes the sentiment value corresponding to the target theme. In addition, this article analyzes the sentiment changes of its individual topics one by one in combination with the specific economic environment. The simulation study shows that the agricultural economic risk forecast system based on data mining technology proposed in this paper has a good effect. Hindawi 2022-04-27 /pmc/articles/PMC9068313/ /pubmed/35528359 http://dx.doi.org/10.1155/2022/3684736 Text en Copyright © 2022 Lei Wang and Hongwei Tan. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wang, Lei
Tan, Hongwei
Agricultural Economic Risk Forecast Based on Data Mining Technology
title Agricultural Economic Risk Forecast Based on Data Mining Technology
title_full Agricultural Economic Risk Forecast Based on Data Mining Technology
title_fullStr Agricultural Economic Risk Forecast Based on Data Mining Technology
title_full_unstemmed Agricultural Economic Risk Forecast Based on Data Mining Technology
title_short Agricultural Economic Risk Forecast Based on Data Mining Technology
title_sort agricultural economic risk forecast based on data mining technology
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9068313/
https://www.ncbi.nlm.nih.gov/pubmed/35528359
http://dx.doi.org/10.1155/2022/3684736
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