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 …