A Comparative Study of Gaussian Process Machine Learning and Time Series Analysis Techniques for Predicting Unemployment Rate

A Comparative Study of Gaussian Process Machine Learning and Time Series Analysis Techniques for Predicting Unemployment Rate

This study explores and compares the capability of Gaussian process machine learning (GPML) with time series analysis techniques, which are autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA) and cubic spline interpolation, in modeling unemployment rate in Malaysia over the per...

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Published in:2024 16th International Conference on Computer and Automation Engineering, ICCAE 2024
Main Author: Aris M.N.M.; Nagaratnam S.; Zakaria N.N.; Mohd Azami M.F.A.; Samsudin M.A.I.; Othman E.S.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198357359&doi=10.1109%2fICCAE59995.2024.10569432&partnerID=40&md5=e74843de516322667c0a3e37be73911f
id 2-s2.0-85198357359
spelling 2-s2.0-85198357359
Aris M.N.M.; Nagaratnam S.; Zakaria N.N.; Mohd Azami M.F.A.; Samsudin M.A.I.; Othman E.S.
A Comparative Study of Gaussian Process Machine Learning and Time Series Analysis Techniques for Predicting Unemployment Rate
2024
2024 16th International Conference on Computer and Automation Engineering, ICCAE 2024


10.1109/ICCAE59995.2024.10569432
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198357359&doi=10.1109%2fICCAE59995.2024.10569432&partnerID=40&md5=e74843de516322667c0a3e37be73911f
This study explores and compares the capability of Gaussian process machine learning (GPML) with time series analysis techniques, which are autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA) and cubic spline interpolation, in modeling unemployment rate in Malaysia over the period of 1991 to 2022. Six predictive models are developed based on the observations. The predictive performance of each model is quantified using mean absolute error (MAE) and mean squared error (MSE). GPML demonstrated the best predictive model, exhibiting minimal MAE and MSE compared to the time series analysis techniques. The statistical analysis also concluded no significant mean difference between the GPML model and the actual observations, implying a robust predictive model in predicting the unemployment rate in Malaysia. © 2024 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper
author Aris M.N.M.; Nagaratnam S.; Zakaria N.N.; Mohd Azami M.F.A.; Samsudin M.A.I.; Othman E.S.
spellingShingle Aris M.N.M.; Nagaratnam S.; Zakaria N.N.; Mohd Azami M.F.A.; Samsudin M.A.I.; Othman E.S.
A Comparative Study of Gaussian Process Machine Learning and Time Series Analysis Techniques for Predicting Unemployment Rate
author_facet Aris M.N.M.; Nagaratnam S.; Zakaria N.N.; Mohd Azami M.F.A.; Samsudin M.A.I.; Othman E.S.
author_sort Aris M.N.M.; Nagaratnam S.; Zakaria N.N.; Mohd Azami M.F.A.; Samsudin M.A.I.; Othman E.S.
title A Comparative Study of Gaussian Process Machine Learning and Time Series Analysis Techniques for Predicting Unemployment Rate
title_short A Comparative Study of Gaussian Process Machine Learning and Time Series Analysis Techniques for Predicting Unemployment Rate
title_full A Comparative Study of Gaussian Process Machine Learning and Time Series Analysis Techniques for Predicting Unemployment Rate
title_fullStr A Comparative Study of Gaussian Process Machine Learning and Time Series Analysis Techniques for Predicting Unemployment Rate
title_full_unstemmed A Comparative Study of Gaussian Process Machine Learning and Time Series Analysis Techniques for Predicting Unemployment Rate
title_sort A Comparative Study of Gaussian Process Machine Learning and Time Series Analysis Techniques for Predicting Unemployment Rate
publishDate 2024
container_title 2024 16th International Conference on Computer and Automation Engineering, ICCAE 2024
container_volume
container_issue
doi_str_mv 10.1109/ICCAE59995.2024.10569432
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198357359&doi=10.1109%2fICCAE59995.2024.10569432&partnerID=40&md5=e74843de516322667c0a3e37be73911f
description This study explores and compares the capability of Gaussian process machine learning (GPML) with time series analysis techniques, which are autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA) and cubic spline interpolation, in modeling unemployment rate in Malaysia over the period of 1991 to 2022. Six predictive models are developed based on the observations. The predictive performance of each model is quantified using mean absolute error (MAE) and mean squared error (MSE). GPML demonstrated the best predictive model, exhibiting minimal MAE and MSE compared to the time series analysis techniques. The statistical analysis also concluded no significant mean difference between the GPML model and the actual observations, implying a robust predictive model in predicting the unemployment rate in Malaysia. © 2024 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
format Conference paper
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record_format scopus
collection Scopus
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