A carregar...
Publicação
Forecasting travel assistance demand with Machine Learning
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 4.15 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
Most vehicles on the road today include some form of travel assistance through an insurance company.
Travel assistance companies work 24/7 at the behest of their clients to ensure that, in case of breakdown or
accident, their vehicles can be safely repaired, on the road or in a garage. To ensure adequate service levels
and quick response times, companies in the travel assistance domain need to have concrete historical
knowledge on a multitude of indicators, including the number of assistance requests per/day, their hourly
distribution, the types of breakdowns, etc. All these factors constitute the basis on which daytoday
planning is thought out, from the number of towtrucks required, the number of contactcentre operators
available their schedule, information systems capacity, etc. In this thesis real data from a company in the
assistance domain is processed, analysed and prepared to feed into several forecasting models, from the
most basic ‘moving average’ model, to complex deep learning model to both evaluate their comparative
requirements and overall performance. Models, from the least to the most complex are examined in the
literature review section as well as particular aspects of timeseries data and what do those aspects entail
in a forecasting exercise. The goal is to understand how well the ‘novel’ machine learning models such
as those based on recurrent neural networks compare to more established statistical approaches such as
SARIMA (Seasonal, AutoRegressive, Integrated, Moving Average). The knowledge gained from this
exercise will serve as the foundation for future works in timeseries prediction.
Descrição
Tese de Mestrado, Ciência de Dados, 2022, Universidade de Lisboa, Faculdade de Ciências
Palavras-chave
SérieTemporal Aprendizagem Automática Assistência em Viagem Deep Learning Teses de mestrado - 2023