Supervisor
Dr Maqsood Hussain Shah
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
Economic disruptions pose challenges for time series forecasting, but also offer opportunities to evaluate the value of exogenous information. This study investigates the role of external indicators during the COVID-19 pandemic and the 2025 U.S. tariff shock, using data from Dell (technology) and Boeing (aerospace/manufacturing). ARIMA, SARIMA, and LSTM models were tested with candidate exogenous variables. Results indicate that the benefit of external signals is highly context-dependent: sector, disruption type, and forecast horizon all influence effectiveness. LSTM models generally excel at longer horizons with exogenous inputs, while ARIMA-based models perform better short-term. Findings emphasize that careful variable selection, ongoing evaluation, and context-awareness are critical for effective forecasting, highlighting the limits of model sophistication alone.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Kavanagh, O.
(2025) Exogenous Variables in Time Series Forecasting During Economic Volatility: A Cross-Sector and Cross-Crisis Evaluation. CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.99