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Predicting the unemployment rate and energy poverty levels in selected European Union countries using an ARIMA-ARNN model

This article analyzes the correlation between energy poverty percentage and unemployment rate for four European countries, Bulgaria, Hungary, Romania and Slovakia, comparing the results with the European average. The time series extracted from the datasets were imported in a hybrid model, namely ARI...

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Autores principales: Popirlan, Claudiu Ionut, Tudor, Irina-Valentina, Popirlan, Cristina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10403200/
https://www.ncbi.nlm.nih.gov/pubmed/37547414
http://dx.doi.org/10.7717/peerj-cs.1464
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author Popirlan, Claudiu Ionut
Tudor, Irina-Valentina
Popirlan, Cristina
author_facet Popirlan, Claudiu Ionut
Tudor, Irina-Valentina
Popirlan, Cristina
author_sort Popirlan, Claudiu Ionut
collection PubMed
description This article analyzes the correlation between energy poverty percentage and unemployment rate for four European countries, Bulgaria, Hungary, Romania and Slovakia, comparing the results with the European average. The time series extracted from the datasets were imported in a hybrid model, namely ARIMA-ARNN, generating predictions for the two variables in order to analyze their interconnectivity. The results obtained from the hybrid model suggest that unemployment rate and energy poverty percentage have comparable tendencies, being strongly correlated. The forecasts suggest that this correlation will be maintained in the future unless appropriate governmental policies are implemented in order to lower the impact of other aspects on energy poverty.
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spelling pubmed-104032002023-08-05 Predicting the unemployment rate and energy poverty levels in selected European Union countries using an ARIMA-ARNN model Popirlan, Claudiu Ionut Tudor, Irina-Valentina Popirlan, Cristina PeerJ Comput Sci Artificial Intelligence This article analyzes the correlation between energy poverty percentage and unemployment rate for four European countries, Bulgaria, Hungary, Romania and Slovakia, comparing the results with the European average. The time series extracted from the datasets were imported in a hybrid model, namely ARIMA-ARNN, generating predictions for the two variables in order to analyze their interconnectivity. The results obtained from the hybrid model suggest that unemployment rate and energy poverty percentage have comparable tendencies, being strongly correlated. The forecasts suggest that this correlation will be maintained in the future unless appropriate governmental policies are implemented in order to lower the impact of other aspects on energy poverty. PeerJ Inc. 2023-07-10 /pmc/articles/PMC10403200/ /pubmed/37547414 http://dx.doi.org/10.7717/peerj-cs.1464 Text en ©2023 Popirlan et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Artificial Intelligence
Popirlan, Claudiu Ionut
Tudor, Irina-Valentina
Popirlan, Cristina
Predicting the unemployment rate and energy poverty levels in selected European Union countries using an ARIMA-ARNN model
title Predicting the unemployment rate and energy poverty levels in selected European Union countries using an ARIMA-ARNN model
title_full Predicting the unemployment rate and energy poverty levels in selected European Union countries using an ARIMA-ARNN model
title_fullStr Predicting the unemployment rate and energy poverty levels in selected European Union countries using an ARIMA-ARNN model
title_full_unstemmed Predicting the unemployment rate and energy poverty levels in selected European Union countries using an ARIMA-ARNN model
title_short Predicting the unemployment rate and energy poverty levels in selected European Union countries using an ARIMA-ARNN model
title_sort predicting the unemployment rate and energy poverty levels in selected european union countries using an arima-arnn model
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10403200/
https://www.ncbi.nlm.nih.gov/pubmed/37547414
http://dx.doi.org/10.7717/peerj-cs.1464
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