Publicação
A synergistic enhancement to demand forecasting using neural networks with voids - streamlining model efficiency through optimizing feature selection
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | pt_PT |
| dc.contributor.advisor | Han, Qiwei | |
| dc.contributor.author | Krogmann, Carl Conrad Günter | |
| dc.date.accessioned | 2024-11-15T11:29:37Z | |
| dc.date.embargo | 2029-01-25 | |
| dc.date.issued | 2024-01-25 | |
| dc.date.submitted | 2024-01-25 | |
| dc.description.abstract | This 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. Keywords: Neural Networks, Demand Forecasting, Feature Engineering, Runtime Optimisation, Demand Shaping, GenAI, Prompt Engineering, Machine Learning, Time Series, Cross-client Data, Temporal Fusion Transforme. | pt_PT |
| dc.identifier.tid | 203605713 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/175288 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | Neural networks | pt_PT |
| dc.subject | Demand forecasting | pt_PT |
| dc.subject | Feature engineering | pt_PT |
| dc.subject | Runtime optimisation | pt_PT |
| dc.title | A synergistic enhancement to demand forecasting using neural networks with voids - streamlining model efficiency through optimizing feature selection | 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 |