Analysis of artificial neural networks training models for airfare price prediction

Abstract

Air transport is playing an increasing role in the world economy every year. This is facilitated by technological development and the latest developments in the aviation industry, globalization. This paper provides an overview of artificial neural network training methods for airfare predicting. The articles for 2017-2019 were analyzed in order to determine the model with the most accurate prediction. The researchers conducted research on open data collected by themselves and set themselves the goal of creating a model that would advise a user the best time to buy a ticket when the price would be the lowest. The review of the papers by similar themes revealed that the Bagging Regression Tree model has the highest results with an accuracy of 88% and the random forest method has an accuracy of 87%. Civil aviation plays an important role in the economy of each country. Aviation is the best way to cover long distances in comfort in the shortest time. Airlines offer customers a variety of opportunities to travel both within the country and abroad. The main problem of interaction between airlines and customers is the airfare: the former want to sell more at the higher price, and the latter want to buy cheaper. Therefore, companies use their own private algorithms for dynamic pricing and constantly monitor the market situation, responsive to changes in demand and the actions of competitors. This behavior allows them to achieve a balance between the desires of airlines and customers. Scientists are trying to invent a way to predict airfare so that air travelers can buy them at the lowest price. The results of the work in this area provide general rules for the best purchase. For example, according to the article (Udachny, 2016) thebest day to buy a ticket by expedia.com for a domestic flight on the United States is Sunday, and the best period is 57 days before departure. This article provides an overview of the works, the authors of which compared the models of machine learning. Achievements in this area are limited to direct flights of a certain domestic market (USA, India) and 88% accuracy of the forecast (Tziridis et al., 2017). The Bagging Regression Tree model described in the article (Tziridis et al., 2017) can be considered the best result. This trained model can make predictions based only on two parameters: the number of free cargo and the number of days left before departure and has an accuracy of 88%.