Supervisor
Taufique Ahmed
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
This study compares three time-series forecasting paradigms—statistical, machine learning, and deep learning—using ten-years of historical weather data from Dublin. The objective is to evaluate the performance of Prophet, XGBoost, and LSTM when forecasting daily solar radiation under multiple preprocessing strategies. An extensive data analysis was conducted, including descriptive statistics, inferential testing, stationarity assessment, outlier detection, feature selection, and exploratory visualisation. These steps revealed strong annual seasonality, nonlinear feature relationships, and variability across meteorological variables, informing the modelling framework and feature-engineering decisions. Four experiments were conducted using cleaned data, differenced data, log-transformed data, and cross-validation. Results show that XGBoost achieved the highest overall accuracy, particularly with the cleaned and log-transformed datasets. Prophet delivered stable, and robust performance, ending in second place. LSTM underperformed relative to the other models, likely due to dataset size and short-term variability. The findings highlight that in data-restricted, highly seasonal environments, statistical and machine learning models outperform deep learning algorithms.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Rios Treviño, E.
(2025) Comparison of Statistical, Machine Learning, and Deep Learning Models for Time-Series Forecasting Using Weather Data from Dublin, Ireland. CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.89