Forecasting Booking Behaviour in the Freight Ferry Industry: An Artificial Neural Network Approach
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2023
Författare
Ränkeskog, Arvid
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The freight ferry industry operates in a stochastic environment where the arrival of
vehicles on the departure day is uncertain. This thesis aimed to explore the application of artificial neural networks (ANNs) to predict whether active bookings at a
given time before departure are going to become transferred, cancelled, or no-show.
The sum of these bookings at a given time before departure was assumed to follow a
Poisson binomial random variable. A comparative analysis was conducted between
ANNs and Gradient Boosting Trees (LightGBM) trained on structured booking
data, such as customer number and check-in status, and recurrent neural networks
(RNNs) trained on a sequence of previous cumulative transfer and cancellation rates.
The findings revealed that LightGBM was the better option to predict the mean of
the distribution, while an ensemble of ANNs demonstrated promising potential in
predicting the distribution’s variance and error estimates.
Beskrivning
Ämne/nyckelord
Artificial neural networks, machine learning, poisson-binomial probability distribution, entity embedding, recurrent neural networks, revenue management, overbooking