Abstract
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.
Original language | English |
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Title of host publication | 2024 16th International Conference on Computer and Automation Engineering (ICCAE) |
Editors | Anouck Girard, Haibin Zhu, Marek Ogiela, Zabih Ghassemlooy |
Place of Publication | USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 242-246 |
Number of pages | 5 |
Edition | 1st |
ISBN (Electronic) | 9798350370058 |
DOIs | |
Publication status | Published - 2024 |
Event | International Conference on Computer and Automation Engineering 2024 - Hybrid, Melbourne, Australia Duration: 14 Mar 2024 → 16 Mar 2024 Conference number: 16th https://ieeexplore.ieee.org/xpl/conhome/10569140/proceeding (Proceedings) https://www.iccae.org/2024.html (Website) |
Conference
Conference | International Conference on Computer and Automation Engineering 2024 |
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Abbreviated title | ICCAE 2024 |
Country/Territory | Australia |
City | Melbourne |
Period | 14/03/24 → 16/03/24 |
Internet address |
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Keywords
- ARIMA
- cubic spline interpolation
- Gaussian process machine learning
- SARIMA
- unemployment rate