Supervisor
Dr. Kashif Qureshi
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
Agricultural price volatility represents a central challenge for the Irish agri-food sector, affecting the stability of producers, cooperatives, and policymakers. This study aimed to compare three predictive approaches applied to strategic commodities such as milk, beef, and potatoes: a traditional statistical time series model (SARIMA) and two deep learning architectures (RNN and LSTM). Using historical price series collected over a decade, the models were developed and evaluated following a rigorous methodological process that included data preparation, algorithm training, and validation of results using performance metrics widely used in time series research. The findings show that the SARIMA model was most effective in series with marked seasonality, such as milk and potatoes, while the LSTM architecture performed better in commodities with more complex fluctuations, such as beef. The RNN model, in contrast, presented limitations in all scenarios analyzed.
As a practical contribution, an interactive tool was developed that integrates the three models and facilitates the visualization of historical prices and future projections, supporting strategic decision-making. Overall, the research demonstrates that model selection depends on the characteristics of each product and that integrating complementary approaches can improve the agricultural sector's resilience to market uncertainty.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Núñez, C.
(2025) Implementation of Time Series and Neural Networks for Forecasting Agricultural Prices in the Irish Market: A Comparative Analysis of Milk, Beef, and Potatoes. CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.107