Deep-Learning-Powered GRU Model for Flight Ticket Fare Forecasting

Abstract

Forecasting flight fares is a critical task in the rapidly expanding civil aviation industry and involves numerous factors. However, traditional airfare prediction systems are ineffective due to the complex and nonlinear relationships of multiple factors, which are not able to accurately account for the impact of different attributes such as time period. To tackle these issues, in this study, we proposed a novel approach that utilizes a deep-learning model, specifically, the Gated Recurrent Unit (GRU), by incorporating 44 decision features. The proposed model is able to capture the intricate relationships between various factors effectively and predict air ticket prices with high accuracy. In the experiments, it was found that the GRU model significantly outperforms not only classic machine learning models but also the MLP and LSTM in terms of assessment indicators of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The GRU model is thus promising concerning the fare prediction of flight tickets.

A Multi-Aspect Informed GRU: A Hybrid Model of Flight Fare Forecasting with Sentiment Analysis

“…Collectively, few studies contribute essential insights into airfare forecasting [1,[18][19][20][21][22]. As methodologies are continuously advanced and refined, the industry can achieve increasingly accurate and efficient fare prediction, which holds potential benefits for both airlines and consumers.…”

“…As methodologies are continuously advanced and refined, the industry can achieve increasingly accurate and efficient fare prediction, which holds potential benefits for both airlines and consumers. Built upon the earlier works, recent studies have made notable strides by utilizing deep learning algorithms to overcome the limitations of traditional prediction models [18]. Leveraging an expansive dataset from Ethiopian Airlines, which spans from January 2018 to July 2022, the researchers integrated 44 decision-making features into a GRU model.…”

“…In predicting airline fares, the typical factors, including season, travel day and month, weekend, holiday, booking class, class of service, and number of stops, are taken into consideration [6,9,18]. However, passenger sentiments on key services may also affect the ticket prices [37][38][39].…”

“…A standout feature of GRU is its swift training pace combined with a diminished risk of overfitting. Such traits become especially beneficial in scenarios with extensive datasets or limited computational resources, as commonly seen in airfare prediction tasks [18].…”

“…Additionally, the GRU model excels at detecting long-term dependencies and complex interrelations in timeseries data [18,43]. This capability facilitates capturing influential factors such as seasonal variation, holiday, and economic oscillation, which may affect the prices over time.…”