Forecasting flight prices with machine learning models

: a comparative analysis between low and high-cost airlines

Abstract

Forecasting fight prices is a challenging task due to the complex nature of the pricing algorithms that airlines use. Apart from the fact that these algorithms are not public, they have to take into account many different variables that affect ticket prices. Since the airlines’ demand forecasting may not always hold true as a result of varying demand, prices need to be adjusted accordingly. This approach is called dynamic pricing. It is a technique of price discrimination based on temporal differences mainly, leading to the widely spread assumption that the time of booking is a crucial determinant of the ticket price. This analysis shows that apart from days to departure, especially fight distance and airline type infuence the price significantly. That is, longer fights as well as fights operated by full-service carriers, as opposed to low-cost carriers, are usually more expensive. This thesis uses a dataset including the fight fares and other fight-related characteristics of one-way fights in the US between April and October 2022, retrieved from the search engine Expedia.com. The data is used to train and compare the performance of several supervised learning models aiming to forecast fight prices. Each model is deployed three times, first with the entire dataset, and then once with data only from low-cost-carrier and only from full-service-carriers, respectively. The most accurate models for all three datasets are the random forests followed by k-nearest-neighbor. The results of this thesis suggest that a large part of the fight price can be predicted using fight-related details such as days to departure and fight duration, yet, it also shows that there remains a certain inexplicable variability that could be due to external factors that are not included in the present analysis.

Date of Award	18 Oct 2023
Original language	English
Awarding Institution	Universidade Católica Portuguesa
Supervisor	Pedro Afonso Fernandes (Supervisor)