Publicação
A synergistic enhancement to demand forecasting using neural networks with Voids
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | pt_PT |
| dc.contributor.advisor | Han, Qiwei | |
| dc.contributor.author | Lenisa, Meeka Hanna | |
| dc.date.accessioned | 2024-10-17T16:12:23Z | |
| dc.date.embargo | 2029-01-25 | |
| dc.date.issued | 2024-01-25 | |
| dc.date.submitted | 2023-12 | |
| dc.description.abstract | This paper presents a collaborative effort encompassing four key individual contributions: optimising feature selection in Temporal Fusion Transformers, enhancing anomaly detection during special events, examining the impact of Cross-Client data integration on forecasting accuracy, and leveraging Generative AI for strategic business recommendations. Collectively, these studies reveal significant advancements in demand forecasting and management for e-commerce companies. The results demonstrate improved predictive accuracy, efficient anomaly handling during critical sales periods, insights into the benefits and limitations of aggregated data models, and advantages of using generative AI for recommending business action to mitigate operational risks. | pt_PT |
| dc.identifier.tid | 203605691 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/173644 | |
| dc.language.iso | eng | pt_PT |
| dc.relation | 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 |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Machine Learning | pt_PT |
| dc.subject | Neural Network | pt_PT |
| dc.subject | Demand Forecasting | pt_PT |
| dc.subject | Time Series | pt_PT |
| dc.title | A synergistic enhancement to demand forecasting using neural networks with Voids | pt_PT |
| dc.title.alternative | ensemble method for anomaly detection and handling | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | embargoedAccess | 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 |