Abstract:

With the rise in popularity of deep learning models across engineering disciplines, their application has piqued considerable interest in economic and financial sectors. This research project aims to harness the potential of deep learning models to unravel the intricate and nonlinear characteristics underlying crude oil price movements. We propose an innovative hybrid forecasting model for crude oil prices, leveraging the capabilities of deep learning architectures. Our research involves an in-depth exploration of significant movements in crude oil prices, coupled with the development and evaluation of the proposed hybrid forecasting model. Real-world price data from the WTI crude oil markets are utilized to validate the model's performance. Through a series of empirical analyses, we demonstrate that our proposed model outperforms traditional forecasting methods, resulting in improved accuracy and precision. This study not only contributes to the broader understanding of the applicability of deep learning models in economic and financial contexts but also offers a practical tool for stakeholders in the energy markets to make informed decisions based on more accurate price forecasts.

Description:

Crude oil, price forecasting, regression techniques, recurrent neural networks