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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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    1. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    2. Sun, Shaolong & Lu, Hongxu & Tsui, Kwok-Leung & Wang, Shouyang, 2019. "Nonlinear vector auto-regression neural network for forecasting air passenger flow," Journal of Air Transport Management, Elsevier, vol. 78(C), pages 54-62.
    3. Sima Siami-Namini & Akbar Siami Namin, 2018. "Forecasting Economics and Financial Time Series: ARIMA vs. LSTM," Papers 1803.06386, arXiv.org.
    4. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    5. Hakim, Md Mahbubul & Merkert, Rico, 2016. "The causal relationship between air transport and economic growth: Empirical evidence from South Asia," Journal of Transport Geography, Elsevier, vol. 56(C), pages 120-127.
    6. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    7. Elton Fernandes & Ricardo Rodrigues Pacheco, 2010. "The causal relationship between GDP and domestic air passenger traffic in Brazil," Transportation Planning and Technology, Taylor & Francis Journals, vol. 33(7), pages 569-581, July.
    8. Baker, Douglas & Merkert, Rico & Kamruzzaman, Md., 2015. "Regional aviation and economic growth: cointegration and causality analysis in Australia," Journal of Transport Geography, Elsevier, vol. 43(C), pages 140-150.
    9. Shu-Chuan Chen & Shih-Yao Kuo & Kuo-Wei Chang & Yi-Ting Wang, 2012. "Improving the forecasting accuracy of air passenger and air cargo demand: the application of back-propagation neural networks," Transportation Planning and Technology, Taylor & Francis Journals, vol. 35(3), pages 373-392, April.
    10. Grosche, Tobias & Rothlauf, Franz & Heinzl, Armin, 2007. "Gravity models for airline passenger volume estimation," Journal of Air Transport Management, Elsevier, vol. 13(4), pages 175-183.
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    Cited by:

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    2. 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).

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