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

Author

Listed:
  • Pavel Baboshkin

    (Financial University Under the Government of the Russian Federation, Moscow, Russia)

  • Mafura Uandykova

    (Narxoz University, Almaty, Republic of Kazakhstan)

Abstract

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.

Suggested Citation

  • Pavel Baboshkin & Mafura Uandykova, 2021. "Multi-source Model of Heterogeneous Data Analysis for Oil Price Forecasting," International Journal of Energy Economics and Policy, Econjournals, vol. 11(2), pages 384-391.
    • Handle: RePEc:eco:journ2:2021-02-46
    as

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    References listed on IDEAS

    as
    1. Uyeh Daniel Dooyum & Alexey Mikhaylov & Igor Varyash, 2020. "Energy Security Concept in Russia and South Korea," International Journal of Energy Economics and Policy, Econjournals, vol. 10(4), pages 102-107.
    2. de Albuquerquemello, Vinícius Phillipe & de Medeiros, Rennan Kertlly & da Nóbrega Besarria, Cássio & Maia, Sinézio Fernandes, 2018. "Forecasting crude oil price: Does exist an optimal econometric model?," Energy, Elsevier, vol. 155(C), pages 578-591.
    3. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
    4. Valeriia Denisova & Alexey Mikhaylov & Evgeny Lopatin, 2019. "Blockchain Infrastructure and Growth of Global Power Consumption," International Journal of Energy Economics and Policy, Econjournals, vol. 9(4), pages 22-29.
    5. Amano, Akihiro, 1987. "A small forecasting model of the world oil market," Journal of Policy Modeling, Elsevier, vol. 9(4), pages 615-635.
    6. Jaehyung An & Alexey Mikhaylov & Nikita Moiseev, 2019. "Oil Price Predictors: Machine Learning Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 9(5), pages 1-6.
    7. Jaehyung An & Alexey Mikhaylov & Sang-Uk Jung, 2020. "The Strategy of South Korea in the Global Oil Market," Energies, MDPI, vol. 13(10), pages 1-8, May.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    artificial neural network; oil forecasting; machine learning; price prediction; energy resources.;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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