A Comparative Study of Gaussian Process Machine Learning and Time Series Analysis Techniques for Predicting Unemployment Rate-Reference-Cited by-同舟云学术

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

Published:2024-03-14 Issue: Volume:4 Page:242-246
ISSN:
Container-title:2024 16th International Conference on Computer and Automation Engineering (ICCAE)
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Author:

Aris Muhammad Naeim Mohd¹,Nagaratnam Shalini²,Zakaria Nurul Nnadiah³,Mohd Azami Muhammad Fadhirul Anuar⁴,Samsudin Muhammad Afiq Ikram⁵,Othman Ernee Sazlinayati⁶

Affiliation:

1. School of American Education, Sunway University,Department of American Degree Transfer Program,Bandar Sunway,Malaysia

2. School of Business Monash University Malaysia,Department of Econometrics and Business Statistics,Bandar Sunway,Malaysia

3. Universiti Teknologi PETRONAS,Faculty of Science and Information Technology,Department of Fundamental and Applied Sciences,Seri Iskandar,Malaysia

4. School of American Education, Sunway University,Department of American Degree Transfer Programme,Bandar Sunway,Malaysia

5. College of Computing, Informatics and Mathematics, Universiti Teknologi MARA,Seremban 3,Malaysia

6. Centre of Foundation Studies, Universiti Teknologi MARA Cawangan Selangor, Kampus Dengkil,Dengkil,Malaysia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10569140/10569143/10569432.pdf?arnumber=10569432

Reference22 articles.

1. A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study

2. An Exploration of Machine Learning Models to Forecast the Unemployment Rate of South Africa: A Univariate Approach

3. Gaussian Processes for Machine Learning

4. Battery health prediction under generalized conditions using a Gaussian process transition model