Forecasting is critically important in the aviation industry for analyzing booking trends, demand forecasting, revenue maximization, and strategic planning. Inaccurate forecasts lead to capacity losses, increased operating costs, and financial drainage for airlines.
This study employs multiple time series forecasting methods including ARIMA, SARIMA, ETS, X11+ETS, X11+ARIMA, STL+ARIMA, STL+RW-drift, and the machine learning technique LSTM on monthly passenger data of Biman Bangladesh Airlines from January 2017 to July 2025. The time series exhibits non-stationarity, upward trend, moderate seasonal strength (Fs = 0.4115), and structural breaks due to COVID-19.
STL decomposition with ARIMA and Random Walk with Drift methods performed optimally for traffic movement forecasting. Seasonal patterns significantly influence revenue and passenger volumes, requiring capacity adjustments. LSTM captures long-term dependencies but underperforms with smaller datasets. Approximately 41% of data variation is attributable to seasonal components. STL+ARIMA achieved the best performance with RMSE of 13,016.25 and MAPE of 4.68% on test data. Seasonal sub-series analysis revealed strong seasonal patterns in air passenger traffic and it depends on direction of the movement of the journey and month of the year.
The study demonstrates that decomposition methods combined with traditional forecasting techniques provide superior accuracy for airline passenger forecasting. The findings offer practical guidance for model selection based on data complexity, computational resources, and application requirements, enabling smarter strategic planning through predictive, data-backed intelligence. Accurate forecasting about market is required to optimize use of capacity of the equipment and render better services to the customers.
Accurate forecasting is not merely a technical exercise but a strategic imperative in the airline industry. The thin profit margins, perishable inventory, and intense competition characteristic of the industry make forecasting accuracy a critical determinant of financial performance.
- Quote paper
- Promitosh Talukder (Author), 2025, Forecasting Passenger and Sales Revenue for an Airline Using a Comparative Analysis of Different Time Series Models, Munich, GRIN Verlag, https://www.grin.com/document/1746485
