Enhancing Oil Price Forecasting Through an Intelligent Hybridized Approach

Abstract

The oil market has long experienced price fluctuations driven by diverse factors. These shifts in crude oil prices wield substantial influence over the costs of various goods and services. Moreover, the price per barrel is intricately intertwined with global economic activities, themselves influenced by the trajectory of oil prices. Analyzing oil behavior stands as a pivotal means for tracking the evolution of barrel prices and predicting future oil costs. This analytical approach significantly contributes to the field of crude oil price forecasting. Researchers and scientists alike prioritize accurate crude oil price forecasting. Yet, such endeavors are often challenged by the intricate nature of oil price behavior. Recent times have witnessed the effective employment of various approaches, including Hybrid and Machine Learning techniques to address similarly complex tasks, though they often yield elevated error rates, as observed in financial markets. In this study, the goal is to enhance the predictive precision of several weak supervised learning predictors by harnessing hybridization, particularly within the context of the crude oil market's multifaceted variations. The focus extends to a vast dataset encompassing CPSE Stock ETF prices over a period of 23 years. Ten distinct models, namely SVM, XGBoost, Random Forest, KNN, Gradient Boosting, Decision Tree, Ridge, Lasso, Elastic Net, and Neural Network, were employed to derive elemental predictions. These predictions were subsequently amalgamated via Linear Regression, yielding heightened performance. The investigation underscores the efficacy of hybridization as a strategy. Ultimately, the proposed approach's performance is juxtaposed against its individual weak predictors, with experiment results validating the findings.