Comparative analysis of neural networks techniques to forecast Airfare Prices

With the growth of tourism industry, airplanes have became an affordable choice for medium- and long-distance travels. Accurate forecasting of flights tickets helps the aviation industry to match demand, supply flexibly and optimize aviation resources. Airline companies use dynamic pricing strategies to determine the price of airline tickets to maximize profits. Passengers want to purchase tickets at the lowest selling price for the flight of their choice. However, airline tickets are a special commodity that is time-sensitive and scarce, and the price of airline tickets is affected by various factors. Our research work provides a systematic comparison of various traditional machine learning methods (i.e., Ridge Regression, Lasso Regression, K-Nearest Neighbor, Decision Tree, XGBoost, Random Forest) and deep learning methods (e.g., Fully Connected Networks, Convolutional Neural Networks, Transformer) to address the problem of airfare prediction, by keeping the consumers’ needs. Moreover, we proposed innovative Bayesian neural networks, which represent the first exploitation attempt of Bayesian Inference for the airfare prediction task, to the best of our knowledge. Therefore, we evaluate the performance of our implemented and optimized models on an open dataset. The experimental results show that deep learning-based methods achieve better results on average than traditional ones, while Bayesian neural networks can achieve better performance among the other machine learning methods. However, taking into account both prediction performance and computational time, the Random Forest turns out to be the best choice to apply in this scenario.