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Deep learning models for forecasting aviation demand time series

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Abstract

The analysis along with the modeling of passenger demand dynamic, which deem to have vital implications on the management and the operation within the entire aviation industry, are regarded to be an extreme challenge. However, air passenger demand introduces reliably complex non-linearity and non-stationarity. In this paper, we have tried to forecast aviation demand with the use of time series and deep learning techniques. We have developed air travel demand estimation and forecasting models, using classical Autoregressive Integrated Moving Average methods (ARIMA), Seasonal approaches (SARIMA) and Deep Learning Neural Networks (DLNN). Moreover, this research has performed a qualitative comparison of the aforementioned techniques aiming to serve as a guideline toward the choice of the optimal modeling approach. The experimental results have shown that the proposed approaches can provide significant assistance in forecasting air travel demand, by producing both accurate and robust results. Therefore, this approach can be utilized as a tool to be reliably employed for air passenger demand forecasting.

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Notes

  1. https://www.transtats.bts.gov/DATABASES.ASP?Mode_ID=1&Mode_Desc=Aviation&Subject_ID2=0&pn=1.

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Authors and Affiliations

  1. Computer Engineering and Informatics Department, University of Patras, Patras, Greece

    Andreas Kanavos & Christos Makris

  2. Department of Computing, Imperial College London, London, UK

    Fotios Kounelis

  3. Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

Authors
  1. Andreas Kanavos
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  2. Fotios Kounelis
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  3. Lazaros Iliadis
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  4. Christos Makris
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Correspondence to Andreas Kanavos.

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Kanavos, A., Kounelis, F., Iliadis, L. et al. Deep learning models for forecasting aviation demand time series. Neural Comput & Applic 33, 16329–16343 (2021). https://doi.org/10.1007/s00521-021-06232-y

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