Support vector regression model for flight demand forecasting

Abstract

Flight demand forecasting is a particularly critical component for airline revenue management because of the direct influence on the booking limits that determine airline profits. The traditional flight demand forecasting models generally only take day of the week (DOW) and the current data collection point (DCP) adds up bookings as the model input and uses linear regression, exponential smoothing, pick-up as well as other models to predict the final bookings of flights. These models can be regarded as time series flight demand forecasting models based on the interval between the current date and departure date. They fail to consider the early bookings change features in the specific flight pre-sale period, and have weak generalization ability, at last, they will lead to poor adaptability to the random changes of flight bookings. The support vector regression (SVR) model, which is derived from machine learning, has strong adaptability to nonlinear random changes of data and can adaptively learn the random disturbances of flight bookings. In this paper, flight bookings are automatically divided into peak, medium, and off (PMO) according to the season attribute. The SVR model is trained by using the vector composed of historical flight bookings and adding up bookings of DCP in the early stage of the flight pre-sale period. Compared with the traditional models, the priori information of flight is increased. We collect 2 years of domestic route bookings data of an airline in China before COVID-19 as the training and testing datasets, and divide these data into three categories: tourism, business, and general, the numerical results show that the SVR model significantly improves the forecasting accuracy and reduces RMSE compared with the traditional models. Therefore, this study provides a better choice for flight demand forecasting.

Sentiment analysis model for Airline customers’ feedback using deep learning techniques

“…The SVR model improves the prediction accuracy and RMSE compared to traditional models. The test dataset includes 2 years’ worth of data from tourism, business, and public roads in China 18…”

“…The SVR model improves the prediction accuracy and RMSE compared to traditional models. The test dataset includes 2 years' worth of data from tourism, business, and public roads in China 18 The paper contributions are Using customer data from SACF, Models using word embedding (Glove embedding models) to improve the sentiment classification performance have been applied to a series of datasets, extensively utilized for feature extractions and were compassed in the training and validation phase. Finally, a comparative study has been conducted on the SACF data analysis utilizing deep learning for evaluating the performance of the different models and input features, which is RNN, LSTM, GRU, CONV1D, and BERT for application to big datasets in 2019.…”