Dynamic Bayesian Networks for Long-term Forecasting the Crude Oil Price

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This thesis introduces a novel mathematical approach for forecasting oil prices over 60 years, utilizing dynamic Bayesian networks (DBN) combined with artificial intelligence and machine learning. It also applies scenario techniques from business management to enhance predictive accuracy and interpretability of oil price trends.