Random Forest Modeling for Forecasting Economic Growth: A Machine Learning Approach in the Case of Algeria "1964-2022"
| المصدر: | مجلة أبعاد اقتصادية |
|---|---|
| الناشر: | جامعة أمحمد بوقرة بومرداس - كلية العلوم الاقتصادية والتجارية وعلوم التسيير |
| المؤلف الرئيسي: | Messaoudi, Malika (Author) |
| المجلد/العدد: | مج14, ع1 |
| محكمة: | نعم |
| الدولة: |
الجزائر |
| التاريخ الميلادي: |
2024
|
| الشهر: | جوان |
| الصفحات: | 391 - 410 |
| DOI: |
10.36539/1427-014-001-019 |
| ISSN: |
1112-8062 |
| رقم MD: | 1480318 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | EcoLink |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Economic Forecasting | Time Series Analysis | Machine Learning (ML) | Random Forest (RF) | Tree Decision
|
| رابط المحتوى: |
عدد مرات التحميل
1
| المستخلص: |
This research aims to explore the efficacy of machine learning techniques, specifically Random Forest modeling, in forecasting economic growth. The research problem lies in the challenge of accurately predicting economic trends, which is crucial for effective policy formulation and decision-making. The study follows a structured methodology comprising data collection, preprocessing, feature selection, model training, and validation. Results demonstrate the effectiveness of Random Forest modeling in capturing the intricate patterns of economic data and outperforming traditional forecasting methods. This approach offers promising prospects for enhancing the accuracy and reliability of economic growth forecasts, thereby facilitating informed decision-making processes in various sectors. |
|---|---|
| ISSN: |
1112-8062 |
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