## 2 Related Work  
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 airlinesand 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. This model excels over conventional machine learning methods and outclasses such models as multilayer perceptrons (MLP) and long short-term memory (LSTM) networks. Performance metrics including MAE, RMSE, and R\({}^{2}\) demonstrate its efficacy. The study thereby substantiates the potential of GRU-based deep learning models for enhancing the accuracy of airfare predictions, contributing valuable business insights to ongoing discussions in this domain.  
In parallel with ongoing efforts in airfare prediction, another study explored various AI techniques to examine pricing policies across multiple airlines and destinations [1]. Employing an extensive dataset from four airlines, the research implemented 16 different models spanning from machine learning (ML) and deep learning (DL) to quantum machine learning (QML). With accuracies between 89% and 99%, the study broke new ground by first applying QML to airfare price prediction and offering an in-depth investigation into the influence of diverse pricing features. Subsequent research highlighted the complexity and unpredictability of airfares, suggesting that these lead to revenue inefficiencies and customer dissatisfaction [19]. This study proposed an integrated model consisting of neural networks and XGBoost regressor that uses a light gradient boosting machine to minimize the mean absolute error in fare prediction. The analysis was based on flight data spanning from March to June 2019 across multiple airlines. The prediction outcomes can guide the airlines in resource allocation and strategic planning aiming at targeted market sectors. Another investigation contrasted eight regression models, including multilayer perceptrons, generalized regression neural networks, random forest regression trees, etc. [21]. Using a dataset of 1814 flights on a single international route, this study illuminated key factors such as departure and arrival times and luggage allowances. Among the models, bagging regression trees yielded the highest accuracy, closely followed by the random forest methods. Additionally, individual random forest and multilayer perceptron models were further merged using weighted stacking, leading to a composite model with enhanced predictive performance [22]. Employing a dataset of 51,000 records of one-week round-trip flights for three domestic airlines, this research considered a range of 12 variables, including the purchase and departure dates. The composite model demonstrated performance gains of 4.4% and 7.7% over the individual random forest and multilayer perceptron models, respectively, as gauged by the R\({}^{2}\) metric.  
Moreover, another research model was presented to forecast the lowest possible fares for specific flight itineraries [23]. A unique ensemble learning algorithm was designed to leverage historical price variations to predict future fare adjustments. This recursive model iteratively utilizes prior predictions to inform subsequent ones and incorporates such features as similar itineraries and the temporal factors like the day of the week. Experimental results indicated that this model is particularly effective for those routes featuring independent pricing behaviors across different flights. Comparative assessments revealed that this ensemble-based approach outperformed the K-nearest neighbors (KNN) algorithm.