Machine learning methodologies for airfare prediction

With the booming tourism industry, more and more people are choosing airplanes as a means of transportation for long-distance travel. Accurate low-price forecasting of air tickets helps the aviation industry to match demand and supply flexibly and make full use of aviation resources. Airline companies use dynamic pricing strategies to determine the price of airline tickets to maximize profits when selling airline tickets. Passengers who choose airplanes as a means of transportation 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, such as the departure time of the plane, the number of hours of advance purchase, and the airline flight, so it is difficult for consumers to know the best time to buy a ticket. Deep learning algorithms have made great achievements in various fields in recent years, however, most prior work on airfare prediction problem is based on traditional machine learning methods, thus the performance of deep learning on this problem remains unclear. In this thesis, we did a systematic comparison of traditional machine learning methods (e.g., Ridge Regression, K-Nearest Neighbor, Random Forest) and deep learning methods (e.g., fully connected networks, convolutional neural networks) on the problem of airfare prediction. We evaluate the performance of different methods on an open dataset of 10,683 domestic routes in India from March 2019 to June 2019. The experimental results show that the deep learning-based methods achieve better results than traditional methods, revealing a great potential for the application in airfare prediction.