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Air passenger forecasting using Neural Granger causal Google trend queries

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  • Li Long, Chan
  • Guleria, Yash
  • Alam, Sameer

Abstract

Air passenger forecasting provides important insights for both Governments and Aerospace industries to plan their for their future activities. Google Trends can provide a large database of historical search query frequency which can be used as explanatory variables for air passenger forecasting. This paper explores the use of a Neural Granger Causality model to select the best search query that can forecast arrival air passengers in Singapore Changi Airport. Neural Granger Causality models are an extension of the original Granger Causality model that uses neural networks instead of Linear Vector Auto-Regressive (VAR) models to capture non-linear relations between the targets and the tested explanatory variables. In this paper, 1317 Google Trends search queries are tested for Neural Granger Causality of which 171 queries are deemed as Neural Granger Causal for forecasting Singapore Changi Airport monthly arrival passengers. The model that used all 171 Neural Granger Queries achieved the highest R2 value (R2=0.919) with the lowest Standard Deviation (SD=0.363) compared to the other models which was not filtered for Neural Granger Causality. The 171 queries found are search terms that reflects a unidirectional neural granger causal relationship with the number of arrival air passengers at Changi Airport.

Suggested Citation

  • Li Long, Chan & Guleria, Yash & Alam, Sameer, 2021. "Air passenger forecasting using Neural Granger causal Google trend queries," Journal of Air Transport Management, Elsevier, vol. 95(C).
    • Handle: RePEc:eee:jaitra:v:95:y:2021:i:c:s0969699721000661
    DOI: 10.1016/j.jairtraman.2021.102083
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    References listed on IDEAS

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    Cited by:

    1. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    2. Ari, Didem & Mizrak Ozfirat, Pinar, 2024. "Comparison of artificial neural networks and regression analysis for airway passenger estimation," Journal of Air Transport Management, Elsevier, vol. 115(C).
    3. Wang, Lu & Ruan, Hang & Hong, Yanran & Luo, Keyu, 2023. "Detecting the hidden asymmetric relationship between crude oil and the US dollar: A novel neural Granger causality method," Research in International Business and Finance, Elsevier, vol. 64(C).
    4. Liang, Xiaozhen & Hong, Chenxi & Chen, Jiaqi & Wang, Yingying & Yang, Mingge, 2024. "A hybrid forecasting architecture for air passenger demand considering search engine data and spatial effect," Journal of Air Transport Management, Elsevier, vol. 118(C).
    5. Bełej Mirosław, 2024. "Exploring Public Interest in Limited-Use Areas and Compensation from Airports in Poland: A Google Trends Analysis," Real Estate Management and Valuation, Sciendo, vol. 32(3), pages 64-76.

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