Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Goldani, Mahdi"'
Autor:
Goldani, Mahdi
This study focuses on predicting the Human Development Index (HDI) trends for GCC countries Saudi Arabia, Qatar, Kuwait, Bahrain, United Arab Emirates, and Omanusing machine learning techniques, specifically the XGBoost algorithm. HDI is a composite
Externí odkaz:
http://arxiv.org/abs/2411.01177
Autor:
Goldani, Mahdi
Political stability is crucial for the socioeconomic development of nations, particularly in geopolitically sensitive regions such as the Gulf Cooperation Council Countries, Saudi Arabia, UAE, Kuwait, Qatar, Oman, and Bahrain. This study focuses on p
Externí odkaz:
http://arxiv.org/abs/2410.21516
Autor:
Goldani, Mahdi
The GCC region includes Saudi Arabia, UAE, Bahrain, Kuwait, Qatar, and Oman, which are of critical geopolitical and economic importance, being rich in oil and positioned along vital maritime routes. However, the region faces complex security challeng
Externí odkaz:
http://arxiv.org/abs/2410.21511
Autor:
Goldani, Mahdi, Tirvan, Soraya Asadi
The Gulf Cooperation Council countries -- Oman, Bahrain, Kuwait, UAE, Qatar, and Saudi Arabia -- holds strategic significance due to its large oil reserves. However, these nations face considerable challenges in shifting from oil-dependent economies
Externí odkaz:
http://arxiv.org/abs/2410.21505
Autor:
Goldani, Mahdi, Tirvan, Soraya Asadi
In predictive modeling, overfitting poses a significant risk, particularly when the feature count surpasses the number of observations, a common scenario in high-dimensional data sets. To mitigate this risk, feature selection is employed to enhance m
Externí odkaz:
http://arxiv.org/abs/2406.04390
Autor:
Goldani, Mahdi
This study explores various feature selection techniques applied to macro-economic forecasting, using Iran's World Bank Development Indicators. Employing a comprehensive evaluation framework that includes Root Mean Square Error (RMSE) and Mean Absolu
Externí odkaz:
http://arxiv.org/abs/2406.03742
Autor:
Ghanbari, Mohammadreza, Goldani, Mahdi
Support vector machine modeling is a new approach in machine learning for classification showing good performance on forecasting problems of small samples and high dimensions. Later, it promoted to Support Vector Regression (SVR) for regression probl
Externí odkaz:
http://arxiv.org/abs/2103.11459
Autor:
Goldani, Mahdi1 mahgoldani@gmail.com
Publikováno v:
Iranian Journal of Finance. 2024, Vol. 8 Issue 1, p47-70. 24p.
Autor:
Ghanbari, Mohammadreza, Goldani, Mahdi
Publikováno v:
Advances in Mathematical Finance & Applications; Spring2022, Vol. 7 Issue 2, p477-487, 11p