IBM Watson Studio for Crude Oil Price Forecasting Using Cloud-Based Techniques

Abstract

Economic planning, risk management, and investment strategies depend on crude oil price predictions. Advanced analytical capabilities in IBM Watson Studio, a cloud-based data science platform, enable reliable forecasting models. The goal is to improve crude oil price forecasts using IBM Watson Studio's machine learning. The goal is to analyze historical pricing patterns, market indicators, and geopolitical effects using cloud-based data processing, predictive modelling, and real-time analytics. We want to create a scalable, automated forecasting system to enhance energy decision-making. IBM Watson Studio uses machine learning and deep learning for data intake, feature engineering, and model training. Scalable cloud deployment allows large-scale data processing and real-time model upgrades. Automated data pipelines reduce human involvement and improve predictions. Predictive analytics improve price trend detection, revealing market variations. Deep learning models, real-time data integration, and adaptive forecasting may improve. Cloud-based forecasting supports proactive market risk mitigation and resource allocation in energy trading, financial planning, and policymaking.