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Forecasting aircraft miles flown time series using a deep learning-based hybrid approach

    Victor Sineglazov Affiliation
    ; Olena Chumachenko Affiliation
    ; Vladyslav Gorbatiuk Affiliation

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.

Keyword : forecasting, neural networks, time series, deep learning, hybrid algorithm, group method of data handling

How to Cite
Sineglazov, V., O. Chumachenko, and V. Gorbatiuk. “Forecasting Aircraft Miles Flown Time Series Using a Deep Learning-Based Hybrid Approach”. Aviation, Vol. 22, May 2018, pp. 6-12, doi:10.3846/aviation.2018.2048.
Published in Issue
May 30, 2018
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This work is licensed under a Creative Commons Attribution 4.0 International License.

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