Supervisor

Taufique Ahmed

Programme

HDIP in Data Analytics for Business

Subject

Computer Science

Abstract

Dublin Airport plays a significant role in Ireland’s tourism and commercial sectors, with passenger demand directly influencing retail, hospitality, car rental and other airport-related businesses. Accurate forecasting of passenger numbers can support these sectors in planning marketing activities, managing stock levels and making evidence-based operational decisions. This project develops a monthly forecasting framework for passenger arrivals at Dublin Airport using data from the Central Statistics Office (CSO). Building on previous exploratory data analysis, the study evaluates five time series forecasting approaches: ARIMA, SARIMA, Holt-Winters Triple Exponential Smoothing, TBATS, and an automated model comparison pipeline using PyCaret. The models are assessed under two experimental scenarios, including and excluding the COVID-19 pandemic period, to examine its impact on forecasting performance. The project is designed as a living forecasting system, allowing models, performance metrics and forecasts to be updated as new monthly CSO data become available. The findings provide a data-driven basis for anticipating passenger demand and supporting commercial and operational planning at Dublin Airport.

Date of Award

2026

Full Publication Date

2026

Access Rights

open access

Document Type

Capstone Project

Resource Type

thesis

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Data Science Commons

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