
Hyperparameter and Feature-Choice Sensitivities: An Application to Predicting Passenger Load in Airports
By: Hendrico BURGER, Busse HEMSKERK, Mathis MOUREY and Jasper VOS
References
- Bandara, K., Hyndman, R. J., and Bergmeir, C. (2021). Mstl: A seasonal-trend decomposition algorithm for time series with multiple seasonal patterns.
- Barnhart, C., Belobaba, P., and Odoni, A. R. (2003). Applications of operations research in the air transport industry. Transportation Science, 37(4):368–391.
- Bergstra, J. and Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13:281–305.
- 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.
- 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.
- Burman, J. P. (1980). Seasonal adjustment by signal extraction. Journal of the Royal Statistical Society. Series A (General), 143(3):321–337.
- 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.
- Cleveland, R. B., Cleveland, W. S., and Terpenning, I. (1990). STL: A seasonal-trend decomposition procedure based on loess. Statistics Sweden (SCB), page 3.
- 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.
- 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.
- 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.
- Eubank, R. L. (1999). Nonparametric Regression and Spline Smoothing. CRC Press, 2nd edition.
- Gelhausen, M. C., Berster, P., and Wilken, D. (2013). Airport capacity constraints and strategies for mitigation. Springer.
- Graham, A. (2014). Managing airports: An international perspective. Routledge.
- Grosche, T., Rothlauf, F., and Heinzl, A. (2007). Gravity models for airline passenger volume estimation. Journal of Air Transport Management, 13(4):175–183.
- Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
- Henderson, P., Islam, R., Bachman, P., et al. (2018). Deep reinforcement learning that matters. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).
- 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.
- Jacquillat, A. and Odoni, A. R. (2015). An integrated scheduling and operations approach to airport congestion mitigation. Operations Research, 63(6):1390–1410.
- 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.
- Ladiray, D. and Quenneville, B. (2001). Outline of the X-11 Method, pages 13–22. Springer New York, New York, NY.
- 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.
- 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.
- Postorino, M. N. and Mantecchini, L. (2010). Development of regional airports in eu. Transportation Research Procedia, pages 27–51.
- 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.
- 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.
- 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.
- Sims, C. A. (1974). Seasonality in regression. Journal of the American Statistical Association, 69(347):618–626.
- 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.
- Taylor, S. J. and Letham, B. (2018). Forecasting at scale. The American Statistician, 72(1):37–45.
- 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.
- 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).
DOI: https://doi.org/10.2478/picbe-2026-0177 | Journal eISSN: 2558-9652
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
Keywords:
Related subjects:
© 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.