ABSTRACT
The goal of this research is to greatly advance forecasting methods in the oil business so that those involved may more confidently grasp new possibilities and handle market risks with skill. This study aims to provide decision-makers with the information they need to maximize resource allocation, reduce risks, and increase profitability in a dynamic industry environment by providing precise and timely projections. Accurate oil demand forecasting is becoming more and more necessary for the oil business to influence price and production plans properly. This work creates a comprehensive forecasting system that combines machine learning and statistical approaches to meet this need. By applying techniques such as Moving Average, ARIMA, VARMA, and EWMA this study attempts to improve the accuracy and reliability of oil demand forecasts. Metrics like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used in a painstaking evaluation procedure to thoroughly evaluate the forecasting models' effectiveness. This evaluation provides insightful information about each methodology's efficacy, enabling stakeholders to choose models with knowledge. The study also explores seasonal patterns in oil price and production, which enhances the forecasting model and promotes a better understanding of market dynamics.
