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- Title
FORECASTING AIRCRAFT MILES FLOWN TIME SERIES USING A DEEP LEARNING-BASED HYBRID APPROACH.
- Authors
SINEGLAZOV, Victor; CHUMACHENKO, Olena; GORBATIUK, Vladyslav
- Abstract
Neural network-based methods such as deep neural networks show great efficiency for a wide range of applications. In this paper, a deep learning-based hybrid approach to forecast the yearly revenue passenger kilometers time series of Australia's major domestic airlines is proposed. The essence of the approach is to use a resilient error backpropagation algorithm with dropout for "tuning" the polynomial neural network, obtained as a result of a multi-layered GMDH algorithm. The article compares the performance of the suggested algorithm on the time series with other popular forecasting methods: deep belief network, multi-layered GMDH algorithm, Box-Jenkins method and the ANFIS model. The minimum reached MAE of the proposed algorithm was approximately 25% lower than the minimum MAE of the next best method - GMDH, thus indicating that the practical application of the algorithm can give good results compared with other well-known methods.
- Subjects
AIRCRAFT accidents; PASSENGER traffic; PRIVATE flying; GMDH algorithms; ARTIFICIAL neural networks
- Publication
Aviation (1648-7788), 2018, Vol 22, Issue 1, p6
- ISSN
1648-7788
- Publication type
Article
- DOI
10.3846/aviation.2018.2048