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.


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    Titel :

    FORECASTING AIRCRAFT MILES FLOWN TIME SERIES USING A DEEP LEARNING-BASED HYBRID APPROACH


    Beteiligte:


    Erscheinungsdatum :

    2018




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




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