Airline ticket price-prediction model based on integrated feature extraction

Abstract

Different feature extraction techniques are used to build AirFare-FS model, which is an integrated ticket price-prediction model, to solve the nonlinear regression problem of ticket price-prediction. Using three public air ticket datasets as an example, the AirFare-FS model identify main features affecting the air ticket price in each dataset and constructs a feature subset of each dataset using eleven feature extraction methods. Then, the AirFare-FS model selects the best feature subset of each dataset using a multi-objective optimization method. Finally, the optimal subset is used to find the best prediction method with the highest matching degree, and the dynamic adaptive model is constructed. The results show that the best feature subset of SixAirlines and EaseMyTrip datasets is subset 4 and the best matching prediction model is gradient descent, while the best subset of flight prices is subset 3 and the best matching prediction model is random forest. The visualization technology is used to show the effect of the characteristics of each optimal feature subset on the ticket price. The results indicate that the flight time dominantly affects the ticket price.