A carregar...
Publicação
Demand shaping in practice - investigating the use of predictive models to identify causal relationships
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 8.21 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
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.
Descrição
Palavras-chave
Demand shaping Forecasting Causal inference Predictive models Causality