This study addresses the air ticket sales forecasting problem for business-to-business (B2B) travel agencies, a largely unexplored area compared to airlines and business-to-consumer agencies. To meet guarantee deposit requirements and manage financial risk exposure, B2B agencies require accurate demand estimation for budget planning and revenue management. The study proposes a hybrid deep learning forecasting model integrating recurrent neural network (RNN), long short-time memory (LSTM) and bidirectional long short-term memory (BiLSTM) architectures to capture both short-term fluctuations and long-term temporal dependencies in ticket sales data.
Approximately eight years (from 3 August 2014 to 2 June 2022) of daily search and ticket sales data from a B2B travel consolidator agency were combined with external variables, including holidays, weather conditions, exchange rates, gross domestic product per capita and COVID-19 cases. Two datasets (with and without pandemic data) were constructed. After preprocessing and Random Forest feature selection, seasonal autoregressive integrated moving average (SARIMA), Prophet, RNN, LSTM and BiLSTM models were implemented and compared with the proposed hybrid architecture using a sliding-window forecasting framework. Performance was evaluated using mean absolute percentage error (MAPE) and root mean squared error (RMSE).
The proposed hybrid model achieved the highest forecasting accuracy across both datasets, outperforming statistical and individual deep learning models. It obtained MAPE values of 5.83% and 6.16% and RMSE values of 0.0379 and 0.0411, respectively. BiLSTM ranked as the second-best method, while SARIMA and Prophet showed substantially lower accuracy. Results also indicated that excluding pandemic variables did not significantly reduce predictive performance, but still, pandemic-related volatility increased the error rate of most of the methods and increased forecasting difficulty.
This research is the first to examine ticket sales forecasting specifically for B2B travel agencies. It introduces a novel hybrid sequential deep learning architecture and evaluates the role of pandemic and macroeconomic variables in forecasting performance. The findings demonstrate the effectiveness of hybrid recurrent models for complex real-world demand forecasting problems.
