Forecasting sales and transactions of fast-food stores: a proof of concept
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/135048 |
Resumo: | Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science |
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Forecasting sales and transactions of fast-food stores: a proof of conceptMachine LearningForecasting demandTime seriesARIMAFacebook ProphetInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceAs time goes on, more and more clients look for solutions to their data-related problems. During a 9-month internship at the Portuguese consulting company Noesis, a request was presented by a customer that wished to improve the forecasting capabilities of their fast-food chain, on sales and transactions, for four different distribution channels, and globally. Following a data analytics approach, hundreds of time series were examined, external variables were added, and two algorithms were used - ARIMA and Facebook’s Prophet. Both models were evaluated, and as each of them performed better in different segments, a hybrid system was implemented, successfully completing the task at hand. Based on the results, future improvements and recommendations were also identified.Castelli, MauroLopes, Pedro FreitasRUNMousinho, Cristina Isabel Palma2022-03-23T14:23:24Z2022-01-282022-01-28T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/135048TID:202970973enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:13:30Zoai:run.unl.pt:10362/135048Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:48:17.830191Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Forecasting sales and transactions of fast-food stores: a proof of concept |
title |
Forecasting sales and transactions of fast-food stores: a proof of concept |
spellingShingle |
Forecasting sales and transactions of fast-food stores: a proof of concept Mousinho, Cristina Isabel Palma Machine Learning Forecasting demand Time series ARIMA Facebook Prophet |
title_short |
Forecasting sales and transactions of fast-food stores: a proof of concept |
title_full |
Forecasting sales and transactions of fast-food stores: a proof of concept |
title_fullStr |
Forecasting sales and transactions of fast-food stores: a proof of concept |
title_full_unstemmed |
Forecasting sales and transactions of fast-food stores: a proof of concept |
title_sort |
Forecasting sales and transactions of fast-food stores: a proof of concept |
author |
Mousinho, Cristina Isabel Palma |
author_facet |
Mousinho, Cristina Isabel Palma |
author_role |
author |
dc.contributor.none.fl_str_mv |
Castelli, Mauro Lopes, Pedro Freitas RUN |
dc.contributor.author.fl_str_mv |
Mousinho, Cristina Isabel Palma |
dc.subject.por.fl_str_mv |
Machine Learning Forecasting demand Time series ARIMA Facebook Prophet |
topic |
Machine Learning Forecasting demand Time series ARIMA Facebook Prophet |
description |
Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-03-23T14:23:24Z 2022-01-28 2022-01-28T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/135048 TID:202970973 |
url |
http://hdl.handle.net/10362/135048 |
identifier_str_mv |
TID:202970973 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799138084592287744 |