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A synergistic enhancement to demand forecasting using neural networks with voids - shaping demand and recommending strategic actions with gen A I

datacite.subject.fosCiências Sociais::Economia e Gestãopt_PT
dc.contributor.advisorHan, Qiwei
dc.contributor.authorDewever, Ines
dc.date.accessioned2024-11-21T18:20:03Z
dc.date.embargo2029-12-25
dc.date.issued2024-01-25
dc.date.submitted2023-12-25
dc.description.abstractThis paper presents a collaborative effort encompassing four key individual contributions: optimising feature selection in Temporal Fusion Transformers, enhancing anomaly de tection during special events, examining the impact of Cross-Client data integration on forecasting accuracy, and leveraging Generative AI for strategic business recommenda tions. Collectively, these studies reveal significant advancements in demand forecasting and management for e-commerce companies. The results demonstrate improved predic tive 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.tid203605730pt_PT
dc.identifier.urihttp://hdl.handle.net/10362/175602
dc.language.isoengpt_PT
dc.subjectDemand shapingpt_PT
dc.subjectGenaipt_PT
dc.subjectNeural networkspt_PT
dc.subjectPrompt engineeringpt_PT
dc.titleA synergistic enhancement to demand forecasting using neural networks with voids - shaping demand and recommending strategic actions with gen A Ipt_PT
dc.typemaster thesis
dspace.entity.typePublication
rcaap.rightsembargoedAccesspt_PT
rcaap.typemasterThesispt_PT
thesis.degree.nameA 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

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