Deep-Learning-Powered GRU Model for Flight Ticket Fare Forecasting
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Forecasting Techniques and Applications · Air Traffic Management and Optimization · Aviation Industry Analysis and Trends
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Information Management, National Taiwan University of Science and Technology, Taipei City 106335, Taiwan
References (51)
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