Publicação
Innovative Applications of Artificial Neural Networks in Tax Forecasting
| Resumo: | The importance of forecasting tax revenues is vital for economic planning and financial sustainability in Mozambique. The study addresses this topic by exploring the potential of Artificial Neural Networks (ANNs) to improve such predictions. The central problem is the limitation of conventional methods in capturing the complexity of fiscal data. The objective is to develop an ANN model that incorporates historical data and economic factors, providing a mixed methodology that enriches the analysis with qualitative and quantitative data. The rationale for adopting ANNs lies in their superior modeling and prediction capabilities in large and complex data environments. The results achieved demonstrate that ANNs can predict tax revenues with greater accuracy, surpassing traditional models. The conclusion points to the ANN as a valuable tool for tax authorities, enhancing efficiency in collection and contributing to the country's fiscal stability. |
|---|---|
| Autores principais: | Rodolfo, Bruno Couto de Abreu |
| Outros Autores: | Gonçalves, Bruno F. |
| Assunto: | Neural networks Tax forecast Sustainability |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| _version_ | 1867173101528154112 |
|---|---|
| author | Rodolfo, Bruno Couto de Abreu |
| author2 | Gonçalves, Bruno F. |
| author2_role | author |
| author_facet | Rodolfo, Bruno Couto de Abreu Gonçalves, Bruno F. |
| author_role | author |
| contributor_name_str_mv | Biblioteca Digital do IPB |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Rodolfo, Bruno Couto de Abreu\"},{\"Person.name\":\"Gonçalves, Bruno F.\",\"Person.identifier.orcid\":\"0000-0002-7541-3673\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Biblioteca Digital do IPB |
| datacite.creators.creator.creatorName.fl_str_mv | Rodolfo, Bruno Couto de Abreu Gonçalves, Bruno F. |
| datacite.date.Accepted.fl_str_mv | 2025-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2025-06-05T10:48:40Z |
| datacite.date.embargoed.fl_str_mv | 2025-06-05T10:48:40Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Neural networks Tax forecast Sustainability |
| datacite.titles.title.fl_str_mv | Innovative Applications of Artificial Neural Networks in Tax Forecasting |
| dc.contributor.none.fl_str_mv | Biblioteca Digital do IPB |
| dc.creator.none.fl_str_mv | Rodolfo, Bruno Couto de Abreu Gonçalves, Bruno F. |
| dc.date.Accepted.fl_str_mv | 2025-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2025-06-05T10:48:40Z |
| dc.date.embargoed.fl_str_mv | 2025-06-05T10:48:40Z |
| dc.description.none.fl_str_mv | A importância da previsão das receitas fiscais é vital para o planeamento econômico e sustentabilidade financeira em Moçambique. Este estudo aborda este tópico explorando o potencial das Redes Neurais Artificiais (RNAs) para melhorar tais previsões. O problema central é a limitação dos métodos convencionais em captar a complexidade dos dados fiscais. A razão para a adoção de RNAs reside na sua superior capacidade de modelação e previsão em ambientes de dados grandes e complexos. Os resultados obtidos demonstram que as RNAs podem prever as receitas fiscais com maior precisão, superando os modelos tradicionais. A conclusão aponta para a RNA como uma ferramenta valiosa para as autoridades fiscais, aumentando a eficiência na cobrança e contribuindo para a estabilidade fiscal do país. |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10198/34559 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Centro Universitário Senac |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Neural networks Tax forecast Sustainability |
| dc.title.fl_str_mv | Innovative Applications of Artificial Neural Networks in Tax Forecasting |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | The importance of forecasting tax revenues is vital for economic planning and financial sustainability in Mozambique. The study addresses this topic by exploring the potential of Artificial Neural Networks (ANNs) to improve such predictions. The central problem is the limitation of conventional methods in capturing the complexity of fiscal data. The objective is to develop an ANN model that incorporates historical data and economic factors, providing a mixed methodology that enriches the analysis with qualitative and quantitative data. The rationale for adopting ANNs lies in their superior modeling and prediction capabilities in large and complex data environments. The results achieved demonstrate that ANNs can predict tax revenues with greater accuracy, surpassing traditional models. The conclusion points to the ANN as a valuable tool for tax authorities, enhancing efficiency in collection and contributing to the country's fiscal stability. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://bibliotecadigital.ipb.pt/bitstreams/b611a2a9-2a9b-41fb-8709-9cfc377cdbff/download |
| id | ipb_4633217d8dfd57ef74e909292cd23255 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10198/34559 |
| instacron_str | ipb |
| institution | Instituto Politécnico de Bragança |
| instname_str | Instituto Politécnico de Bragança |
| language | eng |
| network_acronym_str | ipb |
| network_name_str | Biblioteca Digital do IPB |
| oai_identifier_str | oai:bibliotecadigital.ipb.pt:10198/34559 |
| organization_str_mv | urn:organizationAcronym:ipb |
| person_str_mv | Rodolfo, Bruno Couto de Abreu Gonçalves, Bruno F. Gonçalves, Bruno F. https://www.ciencia-id.pt/311F-2464-A92D 311F-2464-A92D http://orcid.org/0000-0002-7541-3673 0000-0002-7541-3673 |
| publishDate | 2025 |
| publisher.none.fl_str_mv | Centro Universitário Senac |
| reponame_str | Biblioteca Digital do IPB |
| repository_id_str | urn:repositoryAcronym:ipb |
| service_str_mv | urn:repositoryAcronym:ipb |
| spelling | engCentro Universitário SenacengThe importance of forecasting tax revenues is vital for economic planning and financial sustainability in Mozambique. The study addresses this topic by exploring the potential of Artificial Neural Networks (ANNs) to improve such predictions. The central problem is the limitation of conventional methods in capturing the complexity of fiscal data. The objective is to develop an ANN model that incorporates historical data and economic factors, providing a mixed methodology that enriches the analysis with qualitative and quantitative data. The rationale for adopting ANNs lies in their superior modeling and prediction capabilities in large and complex data environments. The results achieved demonstrate that ANNs can predict tax revenues with greater accuracy, surpassing traditional models. The conclusion points to the ANN as a valuable tool for tax authorities, enhancing efficiency in collection and contributing to the country's fiscal stability.porA importância da previsão das receitas fiscais é vital para o planeamento econômico e sustentabilidade financeira em Moçambique. Este estudo aborda este tópico explorando o potencial das Redes Neurais Artificiais (RNAs) para melhorar tais previsões. O problema central é a limitação dos métodos convencionais em captar a complexidade dos dados fiscais. A razão para a adoção de RNAs reside na sua superior capacidade de modelação e previsão em ambientes de dados grandes e complexos. Os resultados obtidos demonstram que as RNAs podem prever as receitas fiscais com maior precisão, superando os modelos tradicionais. A conclusão aponta para a RNA como uma ferramenta valiosa para as autoridades fiscais, aumentando a eficiência na cobrança e contribuindo para a estabilidade fiscal do país.application/pdfengInnovative Applications of Artificial Neural Networks in Tax ForecastingRodolfo, Bruno Couto de AbreuPersonalGonçalves, Bruno F.DSpacehttp://dspace.org/items/b13133cb-538d-45be-b721-2086620fd7aaDSpacehttp://dspace.org/items/b13133cb-538d-45be-b721-2086620fd7aaGonçalvesBruno F.Ciência IDhttps://www.ciencia-id.pt311F-2464-A92DORCIDhttp://orcid.org0000-0002-7541-3673HostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptISSNIsPartOf2237-4558DOIIsPartOf10.22279/navus.v16.19532025-06-05T10:48:40Z20252025-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/34559http://purl.org/coar/access_right/c_abf2open accessNeural networksTax forecastSustainability438538 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal article2025http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/b611a2a9-2a9b-41fb-8709-9cfc377cdbff/downloadNavus-Revista de Gestão e Tecnologia16118 |
| spellingShingle | Innovative Applications of Artificial Neural Networks in Tax Forecasting Rodolfo, Bruno Couto de Abreu Neural networks Tax forecast Sustainability |
| status | SINGLETON |
| subject.fl_str_mv | Neural networks Tax forecast Sustainability |
| title | Innovative Applications of Artificial Neural Networks in Tax Forecasting |
| title_full | Innovative Applications of Artificial Neural Networks in Tax Forecasting |
| title_fullStr | Innovative Applications of Artificial Neural Networks in Tax Forecasting |
| title_full_unstemmed | Innovative Applications of Artificial Neural Networks in Tax Forecasting |
| title_short | Innovative Applications of Artificial Neural Networks in Tax Forecasting |
| title_sort | Innovative Applications of Artificial Neural Networks in Tax Forecasting |
| topic | Neural networks Tax forecast Sustainability |
| topic_facet | Neural networks Tax forecast Sustainability |
| url | http://hdl.handle.net/10198/34559 |
| visible | 1 |