This study investigates the impact of the Russia- Ukraine conflict on Brent Crude commodity pricing using World Bank time series data. The conflict’s influence on global oil and gas markets, characterized by intricate supply and demand dynamics, is analyzed through advanced time series techniques and machine learning modeling. Univariate models such as AutoCorrelation Function (ACF) and Partial AutoCorrelation Function (PACF) are employed to discern temporal patterns in Brent Crude prices. Additionally, Seasonal Autoregressive Inte- grated Moving Average (SARIMA) and Exponential Smoothing State Space (ETS) models are utilized to capture complex seasonality and trends in the data.

Moving beyond traditional methods, multivari ate models are leveraged to comprehensively grasp the multifaceted impact of the conflict. Principal Component Analysis (PCA) and Factor Analysis are applied to uncover latent variables influencing Brent Crude pricing in the context of global trade disruptions, inflation, and diplomatic negotiations. These extracted components are then integrated with ensemble machine learning algorithms, including Random Forest, Extra Tree Classifier, Gradient Boosting, K-Nearest Neighbors, and Decision Trees. The fusion of multivariate time series analysis and machine learning empowers a holistic understanding of the conflict’s intricate repercussions on commodity prices.

Through a comprehensive exploration of time series data and advanced machine learning modeling, this research contributes to a deeper understanding of the intricate relationship between the Russia-Ukraine conflict and global commodity pricing. The findings offer valuable insights for policymakers, industry stakeholders, and investors seeking to navigate the complex landscape of commodity markets during periods of geopolitical instability.