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- W2805344342 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." @default.
- W2805344342 created "2018-06-13" @default.
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- W2805344342 date "2018-05-30" @default.
- W2805344342 modified "2023-09-26" @default.
- W2805344342 title "FORECASTING AIRCRAFT MILES FLOWN TIME SERIES USING A DEEP LEARNING-BASED HYBRID APPROACH" @default.
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- W2805344342 doi "https://doi.org/10.3846/aviation.2018.2048" @default.
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