Using Support Vector Regression to Investigate the Causal Relationship between Petroleum Consumption and Economic Growth in Egypt
| المصدر: | المجلة العلمية للاقتصاد والتجارة |
|---|---|
| الناشر: | جامعة عين شمس - كلية التجارة |
| المؤلف الرئيسي: | Ahmed, Sally Hossam Eldin (Author) |
| مؤلفين آخرين: | Mostafa, Mostafa Galal (Advisor) , Abdel Aal, Medhat Mohamed Ahmed (Advisor) |
| المجلد/العدد: | ع2 |
| محكمة: | نعم |
| الدولة: |
مصر |
| التاريخ الميلادي: |
2022
|
| الشهر: | يوليو |
| الصفحات: | 315 - 334 |
| ISSN: |
2636-2562 |
| رقم MD: | 1373009 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | EcoLink |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Support Vector Regression | Time Series Forecasting | Loss Function | Kernel Function | Empirical Risk Minimization | Structural Risk Minimization | Mean Square Error | Petroleum Consumption | Gasoline | Natural Gas
|
| رابط المحتوى: |
عدد مرات التحميل
3
| LEADER | 02384nam a22002417a 4500 | ||
|---|---|---|---|
| 001 | 2124339 | ||
| 041 | |a eng | ||
| 044 | |b مصر | ||
| 100 | |a Ahmed, Sally Hossam Eldin |e Author |9 727146 | ||
| 245 | |a Using Support Vector Regression to Investigate the Causal Relationship between Petroleum Consumption and Economic Growth in Egypt | ||
| 260 | |b جامعة عين شمس - كلية التجارة |c 2022 |g يوليو | ||
| 300 | |a 315 - 334 | ||
| 336 | |a بحوث ومقالات |b Article | ||
| 520 | |b This paper deals with the application of support vector machine in financial time series forecasting. The paper investigates the causal relationship between disaggregated petroleum consumption and economic growth in Egypt using support vector regression. Petroleum consumption will be disaggregated into six main fuel types: Gasoline, Fuel Oil, Gas Oil, Kerosene, LPG (Liquefied Petroleum Gas) and Natural Gas. The results indicate that SVM provides a promising technique in time series forecasting. For natural gas model MSE in training set =0.005 and in validation set MSE=0.001. For gasoline model MSE in training set =0.003 and for validation set MSE=0.008. For gas oil model MSE in training set =0.004 and in validation set MSE=0.001. For fuel oil model MSE in training set =0.003 and in validation set MSE=0.002. For Kerosene model MSE in training set =0.003and in validation set MSE=0.01. Finally, for LPG model MSE in training set =0.006 and for validation set MSE=0.001, which indicate very high accuracy of the predicted models. | ||
| 653 | |a القطاع الاقتصادي |a استهلاك البترول |a نقل التكنولوجيا |a السلاسل الزمنية | ||
| 692 | |b Support Vector Regression |b Time Series Forecasting |b Loss Function |b Kernel Function |b Empirical Risk Minimization |b Structural Risk Minimization |b Mean Square Error |b Petroleum Consumption |b Gasoline |b Natural Gas | ||
| 700 | |a Mostafa, Mostafa Galal |e Advisor |9 727149 | ||
| 700 | |a Abdel Aal, Medhat Mohamed Ahmed |e Advisor |9 432063 | ||
| 773 | |4 الاقتصاد |4 الإدارة |6 Economics |6 Management |c 030 |e Scientific Journal for Economic & Commerce |f Al-Maġallah Al-ʿilmiyyah Lil-Iqtiṣād Wal Tiğārah |l 002 |m ع2 |o 0527 |s المجلة العلمية للاقتصاد والتجارة |v 052 |x 2636-2562 | ||
| 856 | |u 0527-052-002-030.pdf | ||
| 930 | |d y |p y |q n | ||
| 995 | |a EcoLink | ||
| 999 | |c 1373009 |d 1373009 | ||
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