  The monthly air traffic of a city is a time series with an obvious seasonal
pattern, and is closely related to the economic situation and social
environment of the city. In Hong Kong, for example, July, August, and October
tend to be the peak season of traffic flow, while there is also a relatively
fixed off-season. In the case of a stable social environment, a carefully
identified and fitted seasonal ARIMA model can predict the traffic flow in the
future months well. This work selects the air traffic data, including arrival
and departure passengers of Hong Kong, after the financial crisis and before
the political storm. A sparse seasonal ARIMA$(0,1,1)\times(4,1,0)_{12}$ is
built, which can correctly predict the air traffic from January to July in 2020
within its $95\%$ confidence interval. Furthermore, this work decomposes the
time-series and find that important events, like the financial crisis,
political storm, and the COVID-19 outbreak, affect the level of air traffic to
some extent. For example, the political storm and epidemic prevention and
control that happened after 2019 made the air traffic drop significantly.
According to my sparse seasonal ARIMA model, the air traffic from February to
November in 2020 is only $5\%$ of what it should be without these two events.
This is a valuable application of the time-series model in the air traffic loss
estimation.
