Multi-source Model of Heterogeneous Data Analysis for Oil Price Forecasting

Pavel Baboshkin, Mafura Uandykova


This article sheds light on the question of whether it is possible to create fairly accurate forecasts of real oil prices. For this purpose, a multi-level machine learning model has been created to analyze several sources of heterogeneous data to predict future prices. The article uses different types of data: market condition data, titles, and transaction data. Then, they have been processed to be able to load them into the model. The validation of the regression neural network results showed that the model is more accurate than in previous studies. In fact, this paper presents an artificial neural network model that solves the problem of determining the most informative relationship between different types of oil price data. 

Keywords: artificial neural network, oil forecasting, machine learning, price prediction, energy resources.

JEL Classifications: C45, C51, Q43, Q47


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