Structural Time Series Modelling: Forecasting Air Passenger Numbers

Use of StructTS and dynamic linear modelling

Michael Grogan
7 min readJun 13, 2023
Photo by John McArthur on Unsplash

Note: Original article is available here.


The purpose of this article is to illustrate the use of a Structural Time Series model to forecast air passenger numbers using the Air Traffic Passenger Statistics dataset from DataSF Open Data, which is licensed under the Public Domain and Dedication License (PDDL).

Business Context

The airline industry is very dynamic. As we have seen both pre and post-COVID, passenger demand can change quite quickly.

At the start of the pandemic, passenger numbers collapsed due to travel restrictions. However, the speed at which passenger demand rebounded was equally as surprising.

Forecasting passenger numbers is an important task for an airline — as this will have implications for factors such as what type of aircraft should be deployed on a particular route, estimated fuel costs, expected revenue from a route, among others.

Therefore, an airline that is looking to forecast passenger numbers needs to use a time series model that can quickly react to unanticipated “shocks”. Traditional time series models such as ARIMA…



Michael Grogan

Statistical Data Scientist | Python and R trainer | Financial Writer |