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.
Highlights
Predicting market demand for air transportation is of great significance for airlines, as well as for investors, since the accuracy of such a prediction has a big impact on investment efficiency (Blinova, 2007)
We propose a deep learningbased hybrid approach to forecast the yearly revenue passenger kilometers (RPK) time series of Australia’s major domestic airlines, which are publicly available (Australian Domestic Airline Activity-time series, n.d.)
Group method of data handling (GMDH) is a set of forecasting algorithms that are based on a selection of the best models from the set of trained simple models and the subsequent construction of more complex models using the selected ones
Summary
Predicting market demand for air transportation is of great significance for airlines, as well as for investors, since the accuracy of such a prediction has a big impact on investment efficiency (Blinova, 2007). Airline transportation demand metrics, like Revenue Passenger Kilometers (RPK), are one of the key factors that are considered when preparing an airline’s annual operating plan, performing fleet planning and developing the route network (Ba-Fail, Abed, & Jasimuddin, 2000; Doganis, 2009). Examining and estimating an airline’s transportation demand may likewise help an airline mitigate its risk through an objective assessment of the demand side of the airline business (Abed, Ba-Fail, & Jasimuddin, 2001; Ba-Fail et al, 2000).
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