# 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 (R\({}^{2}\)). The GRU model is thus promising concerning the fare prediction of flight tickets.  
gated recurrent unit; recurrent neural network; flight fare prediction; time series prediction 20231360320231360  
and support vector machines [23], for the prediction of prices. Notice that the trend exhibited by the flight ticket fare is highly complex and nonlinear [24]. Conventional forecasting methods based on a linear model, such as linear regression, therefore faces a number of challenges. A deep neural network that is able to better model highly nonlinear changes is hence promising for such purposes.  
Additionally, financial prediction is complicated due to the fact that there are many features of the series that have varying degrees of influence or importance [13]. Among these features, however, only a few have higher impacts on the trend of the series and convey the most relevant information overall. It is, therefore, necessary for prediction or analysis methods to identify salient and influential features from a large amount of data. For RNN, the gated recurrent unit was shown to be able to prioritize relevant features, allowing for more efficient extraction in short-term mode and preventing data loss due to lengthy time series [25]. Therefore, in this research, we propose the use of the GRU model for RNN, considering its ability to learn to capture correlations between flight ticket fares and relevant features.  
On the other hand, a lack of appropriate data is a major impediment to the advancement of studies on projecting future fares of flight tickets [22]. Since ticket prices are extremely sensitive to business interests, they are seldom disclosed, and the vast majority of airlines do not make their pricing plans known to the public. Despite the limited resources available, a few publications have developed a range of methodologies for predicting ticket prices [26; 27]. In this research, we used historical flight data from Ethiopian Airlines, which is the oldest and leading airline in Africa, for the purpose of this study. The data ranged from January 2018 to July 2022, and there were more than 1,000,000 records from 22 domestic and international airline destinations. The sufficiency and quality of the data make the analysis results credible.  
The key contributions of this study are summarized as follows:  
* The results obtained from the GRU model demonstrate a significant impact and provide valuable insights for ongoing research in this area.
* The research introduces a novel approach employing a deep-learning GRU model for fare prediction. This method addresses the limitations of traditional machine learning techniques that rely heavily on statistical variables in their models.
* The GRU model leverages its unique architecture to capture temporal dependencies in flight data, resulting in improved predictive performance.