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Operations Research Proceedings 2017 pp 81–87Cite as

Long-Term Projections for Commodity Prices—The Crude Oil Price Using Dynamic Bayesian Networks

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Abstract

Long-term projections for commodity prices are a key challenge in science as well as in business environment. This paper proposes a new mathematical approach for future projections of prices for time horizons larger than 10 years using a Dynamic Bayesian Network (DBN). The DBN approach is verified at the crude oil price example.

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References

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  6. Wintershall Holding GmbH. (2017). The “lifecycle” of oil & gas fields. Retrived April 4, 2017 from https://www.wintershall.com/company/about-us/value-chain.html.

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Author information

Authors and Affiliations

  1. Technische Universität Berlin, Berlin, Germany

    Thomas Schwarz & Wilhelm Dominik

  2. Freie Universität Berlin, Berlin, Germany

    Hans-Joachim Lenz

Authors
  1. Thomas Schwarz
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  2. Hans-Joachim Lenz
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  3. Wilhelm Dominik
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Corresponding author

Correspondence to Thomas Schwarz .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Freie Universität Berlin, Berlin, Germany

    Natalia Kliewer

  2. Management Science Group, Otto-von-Guericke Universität Magdeburg, Magdeburg, Germany

    Jan Fabian Ehmke

  3. Department of Mathematics, Freie Universität Berlin, Berlin, Germany

    Ralf Borndörfer

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Cite this paper

Schwarz, T., Lenz, HJ., Dominik, W. (2018). Long-Term Projections for Commodity Prices—The Crude Oil Price Using Dynamic Bayesian Networks. In: Kliewer, N., Ehmke, J., Borndörfer, R. (eds) Operations Research Proceedings 2017. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-89920-6_12

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