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Prediction of Labor Unemployment Based on Time Series Model and Neural Network Model

With the advent of big data, statistical accounting based on artificial intelligence can realistically reflect the dynamics of labor force and market segmentation. Therefore, based on the combination of machine learning algorithm and traditional statistical data under big data, a prediction model of...

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Detalles Bibliográficos
Autores principales: Liu, Xitao, Li, Lihui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9187436/
https://www.ncbi.nlm.nih.gov/pubmed/35694591
http://dx.doi.org/10.1155/2022/7019078
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author Liu, Xitao
Li, Lihui
author_facet Liu, Xitao
Li, Lihui
author_sort Liu, Xitao
collection PubMed
description With the advent of big data, statistical accounting based on artificial intelligence can realistically reflect the dynamics of labor force and market segmentation. Therefore, based on the combination of machine learning algorithm and traditional statistical data under big data, a prediction model of unemployment in labor force based on the combination of time series model and neural network model is built. According to the theoretical parameters, the algorithm of the two-weight neural network is proposed, and the unemployment rate in labor force is predicted according to the weight combination of the two. The outcomes show that the fitting effect based on the combined model is superior to that of the single model and the traditional BP neural network model; at the same time, the prediction results with total unemployment and unemployment rate as evaluation indexes are excellent. The model can offer new ideas for assisting to solve the unemployment of the labor force in China.
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spelling pubmed-91874362022-06-11 Prediction of Labor Unemployment Based on Time Series Model and Neural Network Model Liu, Xitao Li, Lihui Comput Intell Neurosci Research Article With the advent of big data, statistical accounting based on artificial intelligence can realistically reflect the dynamics of labor force and market segmentation. Therefore, based on the combination of machine learning algorithm and traditional statistical data under big data, a prediction model of unemployment in labor force based on the combination of time series model and neural network model is built. According to the theoretical parameters, the algorithm of the two-weight neural network is proposed, and the unemployment rate in labor force is predicted according to the weight combination of the two. The outcomes show that the fitting effect based on the combined model is superior to that of the single model and the traditional BP neural network model; at the same time, the prediction results with total unemployment and unemployment rate as evaluation indexes are excellent. The model can offer new ideas for assisting to solve the unemployment of the labor force in China. Hindawi 2022-06-03 /pmc/articles/PMC9187436/ /pubmed/35694591 http://dx.doi.org/10.1155/2022/7019078 Text en Copyright © 2022 Xitao Liu and Lihui Li. 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
Liu, Xitao
Li, Lihui
Prediction of Labor Unemployment Based on Time Series Model and Neural Network Model
title Prediction of Labor Unemployment Based on Time Series Model and Neural Network Model
title_full Prediction of Labor Unemployment Based on Time Series Model and Neural Network Model
title_fullStr Prediction of Labor Unemployment Based on Time Series Model and Neural Network Model
title_full_unstemmed Prediction of Labor Unemployment Based on Time Series Model and Neural Network Model
title_short Prediction of Labor Unemployment Based on Time Series Model and Neural Network Model
title_sort prediction of labor unemployment based on time series model and neural network model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9187436/
https://www.ncbi.nlm.nih.gov/pubmed/35694591
http://dx.doi.org/10.1155/2022/7019078
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