Skip to main content
Have a personal or library account? Click to login
Hyperparameter and Feature-Choice Sensitivities: An Application to Predicting Passenger Load in Airports Cover

Hyperparameter and Feature-Choice Sensitivities: An Application to Predicting Passenger Load in Airports

Open Access
|Jul 2026

References

  1. Bandara, K., Hyndman, R. J., and Bergmeir, C. (2021). Mstl: A seasonal-trend decomposition algorithm for time series with multiple seasonal patterns.
  2. Barnhart, C., Belobaba, P., and Odoni, A. R. (2003). Applications of operations research in the air transport industry. Transportation Science, 37(4):368–391.
  3. Bergstra, J. and Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13:281–305.
  4. Bergstra, J., Yamins, D., and Cox, D. (2013). Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In International Conference on Machine Learning, pages 115–123.
  5. Bouthillier, X., Delaunay, P., Bronzi, M., et al. (2021). Accounting for variance in machine learning benchmarks. Proceedings of Machine Learning and Systems, 3:747–769.
  6. Burman, J. P. (1980). Seasonal adjustment by signal extraction. Journal of the Royal Statistical Society. Series A (General), 143(3):321–337.
  7. Carson, R. T., Cenesizoglu, T., and Parker, R. (2011). Forecasting (aggregate) demand for us commercial air travel. International Journal of Forecasting, 27(3):923–941.
  8. Cleveland, R. B., Cleveland, W. S., and Terpenning, I. (1990). STL: A seasonal-trend decomposition procedure based on loess. Statistics Sweden (SCB), page 3.
  9. Dagum, E. B. (1978). Modelling, forecasting and seasonally adjusting economic time series with the x-11 arima method. Journal of the Royal Statistical Society. Series D (The Statistician), 27(3/4):203–216.
  10. Dagum, E. B. and Bianconcini, S. (2016). Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation. Statistics for Social and Behavioral Sciences. Springer Cham, 1 edition.
  11. De Livera, A. M., Hyndman, R. J., and Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106(496):1513–1527.
  12. Eubank, R. L. (1999). Nonparametric Regression and Spline Smoothing. CRC Press, 2nd edition.
  13. Gelhausen, M. C., Berster, P., and Wilken, D. (2013). Airport capacity constraints and strategies for mitigation. Springer.
  14. Graham, A. (2014). Managing airports: An international perspective. Routledge.
  15. Grosche, T., Rothlauf, F., and Heinzl, A. (2007). Gravity models for airline passenger volume estimation. Journal of Air Transport Management, 13(4):175–183.
  16. Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
  17. Henderson, P., Islam, R., Bachman, P., et al. (2018). Deep reinforcement learning that matters. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).
  18. Hutter, F., Hoos, H. H., and Leyton-Brown, K. (2011). Sequential model-based optimization for general algorithm configuration. In International Conference on Learning and Intelligent Optimization, pages 507–523. Springer.
  19. Jacquillat, A. and Odoni, A. R. (2015). An integrated scheduling and operations approach to airport congestion mitigation. Operations Research, 63(6):1390–1410.
  20. Klein, R., Koch, S., Steinhardtand, C., and Strauss, A. K. (2020). A review of revenue management: Recent generalizations and advances in industry applications. European Journal of Operational Research, 284(2):397–412.
  21. Ladiray, D. and Quenneville, B. (2001). Outline of the X-11 Method, pages 13–22. Springer New York, New York, NY.
  22. Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., and Talwalkar, A. (2018). Hyperband: A novel bandit-based approach to hyperparameter optimization. Journal of Machine Learning Research, 18(185):1–52.
  23. Martín Rodríguez, G. and Cáceres Hernández, J. J. (2010). Splines and the proportion of the seasonal period as a season index. Economic Modelling, 27(1):83–88.
  24. Postorino, M. N. and Mantecchini, L. (2010). Development of regional airports in eu. Transportation Research Procedia, pages 27–51.
  25. Probst, P., Boulesteix, A.-L., and Bischl, B. (2019). Tunability: Importance of hyperparameters of machine learning algorithms. Journal of Machine Learning Research, 20(53):1–32.
  26. Profillidis, V. A. (2000). Econometric and fuzzy models for the forecast of demand in the airport of rhodes. Journal of Air Transport Management, 6(2):95–100.
  27. Schultz, M., Evler, J., Asadi, E., Preis, H., Fricke, H., and Wu, C.-L. (2020). Future aircraft turnaround operations considering post-pandemic requirements. Journal of Air Transport Management, 89:101886.
  28. Sims, C. A. (1974). Seasonality in regression. Journal of the American Statistical Association, 69(347):618–626.
  29. Snoek, J., Larochelle, H., and Adams, R. P. (2012). Practical bayesian optimization of machine learning algorithms. In Advances in Neural Information Processing Systems 25 (NIPS 2012), pages 2951–2959.
  30. Taylor, S. J. and Letham, B. (2018). Forecasting at scale. The American Statistician, 72(1):37–45.
  31. Tsui, W. H. K., Balli, H. O., and Gower, H. (2011). Forecasting airport passenger traffic: the case of hong kong international airport. Aviation Education and Research Proceedings, 2011:54–62.
  32. Wongsai, N., Wongsai, S., and Huete, A. R. (2017). Annual seasonality extraction using the cubic spline function and decadal trend in temporal daytime modis lst data. Remote Sensing, 9(12).
Language: English
Page range: 2324 - 2340
Published on: Jul 22, 2026
Published by: Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2026 Hendrico BURGER, Busse HEMSKERK, Mathis MOUREY, Jasper VOS, published by Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution 4.0 License.