Publicação
Evaluating the efficacy of gradient boosting algorithms in retail demand forecasting: a case study of triumph international
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | pt_PT |
| dc.contributor.advisor | Belo, Rodrigo | |
| dc.contributor.author | Schirripa, Monica | |
| dc.date.accessioned | 2025-01-02T15:11:47Z | |
| dc.date.available | 2025-01-02T15:11:47Z | |
| dc.date.issued | 2024-01-10 | |
| dc.date.submitted | 2023-12-19 | |
| dc.description.abstract | This thesis is the result of my internship at The Data Cooks, an Amsterdam-based data agency, and aims to enhance demand forecasting for Triumph International, a leading lingerie manufacturer. The research is focused on addressing the complex challenge of disaggregated forecasting in the fashion retail industry, often characterized by a large number of stores and products. Central to this research is the application of gradient boosting algorithms, a cutting edge approach in machine learning. By focusing on methods such as LightGBM and XGBoost, this study delves into the effectiveness of ensemble learning in handling complex, time sensitive data. | pt_PT |
| dc.identifier.tid | 203681835 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/176920 | |
| dc.language.iso | eng | pt_PT |
| dc.relation | UID/ECO/00124/2013 | pt_PT |
| dc.subject | Demand forecasting | pt_PT |
| dc.subject | Machine learning | pt_PT |
| dc.subject | Time-series forecasting | pt_PT |
| dc.subject | Fashion retail | pt_PT |
| dc.subject | Master thesis | pt_PT |
| dc.subject | Gradient boosting | pt_PT |
| dc.title | Evaluating the efficacy of gradient boosting algorithms in retail demand forecasting: a case study of triumph international | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | A Work Project, presented as part of the requirements for the Award of a Master’s degree in Business Analytics from the Nova School of Business and Economics | pt_PT |