Vacancies
| Details | |
|---|---|
| Project Title | Financial Volatility Forecasting using Hybrid Models Integrating Classical Time Series and Machine Learning Approaches |
| Grant | UTARRF 2025 Cycle 1 |
| Campus | Sg Long campus |
| Stipend | RM2,000 per month |
| Entry Requirement | 1. A Bachelor’s degree in Statistics, Mathematics, Actuarial Science, Computer Science, Data Science, or a related field. 2. Must enroll in the Master’s program on a full-time basis. 3. Strong programming skills. 4. Hard working, self-motivated, independent and willing to learn. |
| Job Description | This project aims to develop hybrid models that combine classical time series methods with modern computational techniques to forecast financial volatility. It involves data preprocessing, implementation of statistical models, integration with techniques such as neural networks, and performance evaluation using standard metrics to identify the most effective forecasting approach. |
| Field of Study Related to the Research Project | Computer Science (Master of Science (Mathematics) |
| How to Apply | If you are interested, please contact Dr. Goh Yann Ling via email at gohyl@utar.edu.my |
