Publicação
Demand shaping in practice - investigating the use of predictive models to identify causal relationships
| Resumo: | VOIDS provides deep learning-based demand forecasting. To provide their customers with countermeasures in response to different supply/demand scenarios, VOIDS needs to infer the causal relationship of their clients’ data. This thesis seeks to investigate whether traditional econometric models as well as newer machine learning models can be used to provide VOIDS with a scalable solution for doing causal inference for their clients. The joint part will be focused on theoretical discussions and testing, while individual part 1 compares the results with a prediction model. |
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| Autores principais: | Miotto, Greta |
| Assunto: | Demand shaping Forecasting Causal inference Predictive models Causality |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | VOIDS provides deep learning-based demand forecasting. To provide their customers with countermeasures in response to different supply/demand scenarios, VOIDS needs to infer the causal relationship of their clients’ data. This thesis seeks to investigate whether traditional econometric models as well as newer machine learning models can be used to provide VOIDS with a scalable solution for doing causal inference for their clients. The joint part will be focused on theoretical discussions and testing, while individual part 1 compares the results with a prediction model. |
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