Publicação
NOS & Nova SBE Project-Based Learning - predicting the volume of customers to flag for call center’s specialized team : enhancing the performance of time series models through unsupervised outlier detection and treatment techniques
| Resumo: | This thesis is part of a broader research effort extending the outcomes of the year-long Project-Based Learning initiative with NOS, a prominent telecommunications company in Portugal, focusing on optimizing the number of clients that should be flagged for specialized call center teams, to increase clients’ satisfaction. While the larger thesis addresses both model performance and explainability, this specific work focuses on improving model performance through outlier detection. By refining the handling of outliers, this study contributes to more accurate predictions, ultimately enhancing the effectiveness of client selection. |
|---|---|
| Autores principais: | Muguerza, Victoria |
| Assunto: | Call centers Time series forecasting Prediction modeling Unsupervised outlier detection Trimming Winsorization Robust estimation SARIMA XGBoost |
| Ano: | 2025 |
| 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_ | 1868415042164097024 |
|---|---|
| author | Muguerza, Victoria |
| author_facet | Muguerza, Victoria |
| author_role | author |
| contributor_name_str_mv | Lavado, Susana Pereira, Gustavo RUN |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Muguerza, Victoria\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Lavado, Susana Pereira, Gustavo RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Muguerza, Victoria |
| datacite.date.Accepted.fl_str_mv | 2025-01-29T00:00:00Z |
| datacite.date.available.fl_str_mv | 2025-10-22T10:47:46Z |
| datacite.date.embargoed.fl_str_mv | 2025-10-22T10:47:46Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Call centers Time series forecasting Prediction modeling Unsupervised outlier detection Trimming Winsorization Robust estimation SARIMA XGBoost |
| datacite.titles.title.fl_str_mv | NOS & Nova SBE Project-Based Learning - predicting the volume of customers to flag for call center’s specialized team : enhancing the performance of time series models through unsupervised outlier detection and treatment techniques |
| dc.contributor.none.fl_str_mv | Lavado, Susana Pereira, Gustavo RUN |
| dc.creator.none.fl_str_mv | Muguerza, Victoria |
| dc.date.Accepted.fl_str_mv | 2025-01-29T00:00:00Z |
| dc.date.available.fl_str_mv | 2025-10-22T10:47:46Z |
| dc.date.embargoed.fl_str_mv | 2025-10-22T10:47:46Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/189605 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Call centers Time series forecasting Prediction modeling Unsupervised outlier detection Trimming Winsorization Robust estimation SARIMA XGBoost |
| dc.title.fl_str_mv | NOS & Nova SBE Project-Based Learning - predicting the volume of customers to flag for call center’s specialized team : enhancing the performance of time series models through unsupervised outlier detection and treatment techniques |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_bdcc |
| description | This thesis is part of a broader research effort extending the outcomes of the year-long Project-Based Learning initiative with NOS, a prominent telecommunications company in Portugal, focusing on optimizing the number of clients that should be flagged for specialized call center teams, to increase clients’ satisfaction. While the larger thesis addresses both model performance and explainability, this specific work focuses on improving model performance through outlier detection. By refining the handling of outliers, this study contributes to more accurate predictions, ultimately enhancing the effectiveness of client selection. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | masterThesis |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/c6c46b4e-5c7d-4692-a92e-9f42b616bd05/download |
| id | run_ec45d3fa7fb96e42e66ee6c0bbb9a33f |
| identifier.url.fl_str_mv | http://hdl.handle.net/10362/189605 |
| 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:10362/189605 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Muguerza, Victoria |
| publishDate | 2025 |
| reponame_str | Repositório Institucional da UNL |
| repository_id_str | urn:repositoryAcronym:run |
| service_str_mv | urn:repositoryAcronym:run |
| spelling | engpt_PTThis thesis is part of a broader research effort extending the outcomes of the year-long Project-Based Learning initiative with NOS, a prominent telecommunications company in Portugal, focusing on optimizing the number of clients that should be flagged for specialized call center teams, to increase clients’ satisfaction. While the larger thesis addresses both model performance and explainability, this specific work focuses on improving model performance through outlier detection. By refining the handling of outliers, this study contributes to more accurate predictions, ultimately enhancing the effectiveness of client selection.application/pdfpt_PTNOS & Nova SBE Project-Based Learning - predicting the volume of customers to flag for call center’s specialized team : enhancing the performance of time series models through unsupervised outlier detection and treatment techniquesMuguerza, VictoriaLavado, SusanaPereira, GustavoHostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptURNurn:tid:2039922022025-10-22T10:47:46Z2025-01-292025-01-292025-01-29T00:00:00ZHandlehttp://hdl.handle.net/10362/189605http://purl.org/coar/access_right/c_abf2open accessCall centersTime series forecastingPrediction modelingUnsupervised outlier detectionTrimmingWinsorizationRobust estimationSARIMAXGBoost2289432 bytesliteraturehttp://purl.org/coar/resource_type/c_bdccmaster thesishttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://run.unl.pt/bitstreams/c6c46b4e-5c7d-4692-a92e-9f42b616bd05/download |
| spellingShingle | NOS & Nova SBE Project-Based Learning - predicting the volume of customers to flag for call center’s specialized team : enhancing the performance of time series models through unsupervised outlier detection and treatment techniques Muguerza, Victoria Call centers Time series forecasting Prediction modeling Unsupervised outlier detection Trimming Winsorization Robust estimation SARIMA XGBoost |
| status | SINGLETON |
| subject.fl_str_mv | Call centers Time series forecasting Prediction modeling Unsupervised outlier detection Trimming Winsorization Robust estimation SARIMA XGBoost |
| title | NOS & Nova SBE Project-Based Learning - predicting the volume of customers to flag for call center’s specialized team : enhancing the performance of time series models through unsupervised outlier detection and treatment techniques |
| title_full | NOS & Nova SBE Project-Based Learning - predicting the volume of customers to flag for call center’s specialized team : enhancing the performance of time series models through unsupervised outlier detection and treatment techniques |
| title_fullStr | NOS & Nova SBE Project-Based Learning - predicting the volume of customers to flag for call center’s specialized team : enhancing the performance of time series models through unsupervised outlier detection and treatment techniques |
| title_full_unstemmed | NOS & Nova SBE Project-Based Learning - predicting the volume of customers to flag for call center’s specialized team : enhancing the performance of time series models through unsupervised outlier detection and treatment techniques |
| title_short | NOS & Nova SBE Project-Based Learning - predicting the volume of customers to flag for call center’s specialized team : enhancing the performance of time series models through unsupervised outlier detection and treatment techniques |
| title_sort | NOS & Nova SBE Project-Based Learning - predicting the volume of customers to flag for call center’s specialized team : enhancing the performance of time series models through unsupervised outlier detection and treatment techniques |
| topic | Call centers Time series forecasting Prediction modeling Unsupervised outlier detection Trimming Winsorization Robust estimation SARIMA XGBoost |
| topic_facet | Call centers Time series forecasting Prediction modeling Unsupervised outlier detection Trimming Winsorization Robust estimation SARIMA XGBoost |
| url | http://hdl.handle.net/10362/189605 |
| visible | 1 |