Predicting Crude Oil Price Volatility using Hybrid Artificial Intelligence Models

Abstract

This research investigates the application of hybrid artificial intelligence (AI) models for forecasting crude oil price volatility. The primary objective is to improve the accuracy and reliability of existing forecasting methods by combining the strengths of different AI techniques, such as Recurrent Neural Networks (RNNs) and Support Vector Machines (SVMs). The scope encompasses data preprocessing, model development, training, and evaluation using appropriate metrics. The conclusion highlights the superior performance of the proposed hybrid model compared to individual AI models and traditional statistical methods, demonstrating its potential for practical application in the energy sector.

Introduction

Crude oil price volatility significantly impacts global economies, affecting inflation, energy security, and investment decisions. Accurate price forecasting is crucial for governments, businesses, and consumers. Traditional econometric models often struggle to capture the complex, non-linear relationships inherent in oil price dynamics. AI techniques, with their ability to learn from large datasets and identify complex patterns, offer a promising alternative. However, challenges remain in selecting the most appropriate AI model and optimizing its parameters for accurate and robust forecasting. This research addresses these challenges by exploring the use of hybrid AI models that leverage the strengths of different approaches to achieve superior predictive performance.

Technical Details

• Uses hybrid model combining RNN and SVM
• Data preprocessing includes normalization and noise reduction
• Achieved superior accuracy compared to standalone AI models
• Useful for energy market stakeholders and policymakers