Oil and Gas Industry Price Prediction Using Hybrid Machine Learning Techniques

Abstract

The volatility in oil and gas prices presents a considerable challenge for industry stakeholders, impacting strategic planning, investment choices, and economic projections. This study introduces a hybrid machine learning model that integrates Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, and Natural Language Processing (NLP) to improve the accuracy and dependability of price forecasts in the energy sector. The ANN component identifies complex, nonlinear relationships within historical price data, while LSTM networks focus on capturing temporal dependencies and trends over time. Concurrently, NLP techniques are applied the proposed model leverages unstructured textual data such as news articles, financial disclosures, and social media content to extract sentiment and uncover critical insights. By integrating this qualitative information with quantitative market data, the model captures both internal market behaviors and external influences that affect price fluctuations. Testing on real-world datasets demonstrates that this hybrid methodology surpasses conventional models in terms of predictive accuracy, resilience, and adaptability in volatile market environments. As a result, it offers a powerful decision-support tool for energy analysts, investors, and policymakers aiming to optimize resource distribution and strengthen risk management within the oil and gas sector.