A Blending Model Combined DNN and LightGBM for Forecasting the Sales of Airline Tickets

The main goal of this paper is to forecast the sales of airline tickets in time series affected by many factors including different flights, date features and short, middle, long-term historical sales information. The Deep Neural Network (DNN) model and Light Gradient Boosting Machine (LightGBM) model are combined as a Blending model to forecast the sales of airline tickets in future. Simulation results demonstrate the Blending model is better than DNN and LightGBM evaluated by performance metrics including Mean absolute error (MAE) and Mean squared error (MSE).