Publicação
Forecasting sales and transactions of fast-food stores: a proof of concept
| Resumo: | As 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. |
|---|---|
| Autores principais: | Mousinho, Cristina Isabel Palma |
| Assunto: | Machine Learning Forecasting demand Time series ARIMA Facebook Prophet |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| _version_ | 1868983327106531328 |
|---|---|
| author | Mousinho, Cristina Isabel Palma |
| author_facet | Mousinho, Cristina Isabel Palma |
| author_role | author |
| contributor_name_str_mv | Castelli, Mauro Lopes, Pedro Freitas RUN |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Mousinho, Cristina Isabel Palma\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Castelli, Mauro Lopes, Pedro Freitas RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Mousinho, Cristina Isabel Palma |
| datacite.date.Accepted.fl_str_mv | 2022-01-28T00:00:00Z |
| datacite.date.available.fl_str_mv | 2022-03-23T14:23:24Z |
| datacite.date.embargoed.fl_str_mv | 2022-03-23T14:23:24Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Machine Learning Forecasting demand Time series ARIMA Facebook Prophet |
| datacite.titles.title.fl_str_mv | Forecasting sales and transactions of fast-food stores: a proof of concept |
| dc.contributor.none.fl_str_mv | Castelli, Mauro Lopes, Pedro Freitas RUN |
| dc.creator.none.fl_str_mv | Mousinho, Cristina Isabel Palma |
| dc.date.Accepted.fl_str_mv | 2022-01-28T00:00:00Z |
| dc.date.available.fl_str_mv | 2022-03-23T14:23:24Z |
| dc.date.embargoed.fl_str_mv | 2022-03-23T14:23:24Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/135048 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by-nc/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Machine Learning Forecasting demand Time series ARIMA Facebook Prophet |
| dc.title.fl_str_mv | Forecasting sales and transactions of fast-food stores: a proof of concept |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_bdcc |
| description | As 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. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | masterThesis |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/944618f7-f706-47ab-a894-5fb2df2af895/download |
| id | run_fededa42ee24293be7431ba4e6de7d71 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10362/135048 |
| inst_facet_str | urn:organizationAcronym:unl{{{_:::_}}}Universidade Nova de Lisboa |
| instacron_str | unl |
| institution | Universidade Nova de Lisboa |
| instname_str | Universidade Nova de Lisboa |
| language | eng |
| network_acronym_str | run |
| network_name_str | Repositório Institucional da UNL |
| oai_identifier_str | oai:run.unl.pt:10362/135048 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Mousinho, Cristina Isabel Palma |
| publishDate | 2022 |
| repo_facet_str | urn:repositoryAcronym:run{{{_:::_}}}Repositório Institucional da UNL |
| reponame_str | Repositório Institucional da UNL |
| repository_id_str | urn:repositoryAcronym:run |
| service_str_mv | urn:repositoryAcronym:run |
| spelling | engpt_PTAs 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.application/pdfpt_PTForecasting sales and transactions of fast-food stores: a proof of conceptMousinho, Cristina Isabel PalmaCastelli, MauroLopes, Pedro FreitasHostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptURNurn:tid:2029709732022-03-23T14:23:24Z2022-01-282022-01-28T00:00:00ZHandlehttp://hdl.handle.net/10362/135048http://purl.org/coar/access_right/c_abf2open accessMachine LearningForecasting demandTime seriesARIMAFacebook Prophet3188753 bytesliteraturehttp://purl.org/coar/resource_type/c_bdccmaster thesis2022-01-28http://creativecommons.org/licenses/by-nc/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://run.unl.pt/bitstreams/944618f7-f706-47ab-a894-5fb2df2af895/download |
| 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 |
| status | SINGLETON |
| subject.fl_str_mv | Machine Learning Forecasting demand Time series ARIMA Facebook Prophet |
| title | 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_short | 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 |
| topic | Machine Learning Forecasting demand Time series ARIMA Facebook Prophet |
| topic_facet | Machine Learning Forecasting demand Time series ARIMA Facebook Prophet |
| url | http://hdl.handle.net/10362/135048 |
| visible | 1 |