A Comprehensive study of applying machine learning algorithms for time series data prediction to the Irish Labour Market Unemployment Rate

-, Shree Hari Krishnamurthy (2023) A Comprehensive study of applying machine learning algorithms for time series data prediction to the Irish Labour Market Unemployment Rate. Masters thesis, Dublin, National College of Ireland.

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Abstract

The utilisation of time series data provides significant insights into periodic patterns and the trends, making its study crucial across various fields, such as economic forecasting. This study seeks to examine prediction of the unemployment rate in the Ireland by utilising time series data. The study utilises a dual strategy, incorporating traditional statistical models as well as contemporary machine learning approaches in order to obtain precise predictions. Traditional statistical models like as ARIMA and SARIMA are commonly employed to capture the natural variations in time present in data, including seasonal oscillations. Simultaneously, advanced machine learning models such as Random Forest, Ridge Regression, KNeighbors Regressor (KNN), and XGBoost are utilised to investigate their capabilities in improving the accuracy of predictions. The paper thoroughly assesses various models by considering their performance measures such as the RMSE, R-squared, and MAPE. It conducts an comparison analysis to determine effectiveness of each technique. Among the models studied, Ridge Regression outperformed others, demonstrating promising capabilities in conjunction with the time series data. This extensive research makes a valuable contribution to the existing body of knowledge by exploring the utilisation of statistical and machine learning models to enhance the precision and reliability of unemployment rate predictions.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Trinh, Anh Duong (Senja) UNSPECIFIED
Uncontrolled Keywords:	Time series; Ireland; Unemployment; ARIMA; SARIMA; XGBoost; KNN; Ridge; Random Forest
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HD Industries. Land use. Labor > Issues of Labour and Work > Unemployment
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	07 Nov 2024 16:08
Last Modified:	07 Nov 2024 16:08
URI:	https://norma.ncirl.ie/id/eprint/7164

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