Dynamic combination of forecasts generated by diversification procedures applied to forecasting of airline cancellations
Open accessAbstract
The combination of forecasts is a well established procedure for improving forecast performance and decreasing the risk of selecting an inferior model out of an existing pool of models. Work in this area mainly focuses on combining several functionally different models, but some publications also deal with combining forecasts with the same functional approach. In the latter case, individual forecasts are generated by diversifying one or more model parameters or, if dealing with hierarchical data, by using forecasts from different levels. This work looks at multi-dimensional data from airline industry, with the aim of improving the forecast of cancellation rates for bookings. Three different methods are employed for the generation of individual forecasts. Forecast combinations are usually implemented in a more or less static structure, either including all available forecasts or trimming a fixed percentage of the worst performing models. For a big number of individual forecasts, this procedure can become inefficient. In this paper, a dynamic approach of pooling and trimming is applied to the generated forecasts for airline cancellation data.
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Forecast combinations reduce uncertainties associated with individual models and increase forecast accuracy by exploiting the strengths of the models.
“Forecast combinations helps to reduce the uncertainties associated with individual models and increases forecast accuracy by exploiting the strengths of the models (Lemke et al., 2009).”
From Passenger demand forecasting in scheduled transportation (2020) · cites this paper as Lemke et al., 2009
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- Authors
- Lemke, C., Riedel, S., & Gabrys, B.
- Affiliations
- Bournemouth University (GB)
- Lufthansa (Germany) (DE)
- Venue
- 2009 IEEE Symposium on Computational Intelligence for Financial Engineering
- Publisher
- IEEE
- Published
- 2009-03
- Pages
- 85-91
- OpenAlex
- W2139287124
- Cited by
- 20
- References
- 21
- Citations by year
- 2025: 1 · 2021: 2 · 2020: 4 · 2019: 3 · 2018: 1 · 2017: 2 · 2016: 1 · 2015: 1 · 2013: 1 · 2012: 2
- FWCI
- 2.74
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- IEEE Conference on Computational Intelligence for Financial Engineering & Economics (publishedVersion)
- Bournemouth University Research Online (Bournemouth University) (acceptedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Social Sciences › Decision Sciences › Management Science and Operations Research › Grey System Theory Applications
- Keywords
- forecast combination, booking cancellations, trimming, forecast accuracy, airline industry
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