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The dissertation investigates the application of Probabilistic GraphicalModels (PGMs) in forecasting the price of Crude Oil.  This research is important because crude oil plays a very pivotal role in theglobal economy hence is a very critical macroeconomic indicator of theindustrial growth. Given the vast amount of macroeconomic factors affecting theprice of crude oil such as supply of oil from OPEC countries, demand of oilfrom OECD countries, geopolitical and geoeconomic changes among many othervariables - probabilistic graphical models (PGMs) allow us to understand bylearning the graphical structure. This dissertation proposes condensing datanumerous Crude Oil factors into a graphical model in the attempt of creating aaccurate forecast of the price of crude oil.  The research project experiments with using different libraries in Python inorder to construct models of the crude oil market. The experiments in thisthesis investigate three main challenges commonly presented while trading oilin the financial markets. The first challenge it investigates is the process oflearning the structure of the oil markets; thus allowing crude oil traders tounderstand the different physical market factors and macroeconomic indicatorsaffecting crude oil markets and how they are \textit{causally} related. Thesecond challenge it solves is the exploration and exploitation of the availabledata and the learnt structure in predicting the behaviour of the oil markets.The third challenge it investigates is how to validate the performance andreliability of the constructed model in order for it to be deployed in thefinancial markets.  A design and implementation of a probabilistic framework for forecasting theprice of crude oil is also presented as part of the research.

Application of Probabilistic Graphical Models in Forecasting Crude Oil Price