| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1031390 | Journal of Air Transport Management | 2009 | 5 Pages |
Abstract
An artificial neural forecasting model is developed for air transport passenger analysis. It uses a preprocessing method that decomposes information to reveal relevant features from the data. It is found that neural processing outperforms the traditional econometric approach and offers generalization on time series behavior, even where there are only small samples.
Related Topics
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Authors
K.P.G. Alekseev, J.M. Seixas,