Crude Oil Price Forecasting Using Machine Learning

The oil prices and its future are one of the most prominent topics in our world nowadays. This volume illustrates the usage of machine learning and computational intelligence approaches to predict crude oil prices using direct prediction and combined prediction models. Crude oil price is affected by many factors and coupled by an international network of thousands of producers, refiners, marketers, traders, and consumers for buying and selling physical volumes of crude oil. To understand, organize, analyze and explain the behavior of the crude oil price market and different aspects of international crude oil pricing and prediction, a novel information enterprise architecture for crude oil pricing and prediction is illustrated based on Zachman framework.