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Optimizing Predictor Variables in Artificial Neural Networks When Forecasting Raw Material Prices for Energy Production

Author

Listed:
  • Marta Matyjaszek

    (Doctorate Program on Economics and Enterprise, University of Oviedo, Independencia 13, 33004 Oviedo, Spain)

  • Gregorio Fidalgo Valverde

    (School of Mining, Energy and Materials Engineering, University of Oviedo, Independencia 13, 33004 Oviedo, Spain)

  • Alicja Krzemień

    (Department of Risk Assessment and Industrial Safety, Central Mining Institute, Plac Gwarków 1, 40-166 Katowice, Poland)

  • Krzysztof Wodarski

    (Faculty of Organization and Management, Silesian University of Technology, Roosevelt 26, 41-800 Zabrze, Poland)

  • Pedro Riesgo Fernández

    (School of Mining, Energy and Materials Engineering, University of Oviedo, Independencia 13, 33004 Oviedo, Spain)

Abstract

This paper applies a heuristic approach to optimize the predictor variables in artificial neural networks when forecasting raw material prices for energy production (coking coal, natural gas, crude oil and coal) to achieve a better forecast. Two goals are (1) to determine the optimum number of time-delayed terms or past values forming the lagged variables and (2) to improve the forecast accuracy by adding intrinsic signals to the lagged variables. The conclusions clearly are in opposition to the actual scientific literature: when addressing the lagged variable size, the results do not confirm relationships among their size, representativeness and estimation accuracy. It is also possible to verify an important effect of the results on the lagged variable size. Finally, adding the order in the time series of the lagged variables to form the predictor variables improves the forecast accuracy in most cases.

Suggested Citation

  • Marta Matyjaszek & Gregorio Fidalgo Valverde & Alicja Krzemień & Krzysztof Wodarski & Pedro Riesgo Fernández, 2020. "Optimizing Predictor Variables in Artificial Neural Networks When Forecasting Raw Material Prices for Energy Production," Energies, MDPI, vol. 13(8), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:2017-:d:347314
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    References listed on IDEAS

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    Cited by:

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    2. Xiaojie Xu & Yun Zhang, 2023. "Coking coal futures price index forecasting with the neural network," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(2), pages 349-359, June.
    3. Izabela Zimoch & Ewelina Bartkiewicz & Joanna Machnik-Slomka & Iwona Klosok-Bazan & Adam Rak & Stanislav Rusek, 2021. "Sustainable Water Supply Systems Management for Energy Efficiency: A Case Study," Energies, MDPI, vol. 14(16), pages 1-20, August.
    4. Chenglong Chen & Yikun Liu & Decai Lin & Guohui Qu & Jiqiang Zhi & Shuang Liang & Fengjiao Wang & Dukui Zheng & Anqi Shen & Lifeng Bo & Shiwei Zhu, 2021. "Research Progress of Oilfield Development Index Prediction Based on Artificial Neural Networks," Energies, MDPI, vol. 14(18), pages 1-25, September.
    5. Buelga Díaz, Arturo & Diego Álvarez, Isidro & Castañón Fernández, César & Krzemień, Alicja & Iglesias Rodríguez, Francisco Javier, 2021. "Calculating ultimate pit limits and determining pushbacks in open-pit mining projects," Resources Policy, Elsevier, vol. 72(C).

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