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Predicting coal price using time series methods and combination of radial basis function (RBF) neural network with time series

Author

Listed:
  • Parviz Sohrabi

    (Hamedan University of Technology (HUT))

  • Behshad Jodeiri Shokri

    (Hamedan University of Technology (HUT))

  • Hesam Dehghani

    (Hamedan University of Technology (HUT))

Abstract

This paper estimates the coal prices using two-time series and combined radial basis function (RBF) neural network methods. The time series method was simulated using the Monte Carlo simulation, while the combined RBF neural network method was employed using MATLAB software. The required data, including historical daily coal prices from 2018 to 2020, were collected. This hybrid method has eventually approximated the coal price with acceptable precision concerning the time series method. Due to the high differences among historical data in some periods, the root means squared error (RMSE), 1.49, and the correlation coefficient, 0.93651, of the combined model have provided a better prediction than the time series method RMSE, 2.68, and the correlation coefficient, 0.32. The results revealed that the combination of Brownian motion with mean return (BMMR) and RBF NN model (CBRN) has eventually been able to satisfactorily reduce the error value considering the data differences due to the critical factors, including economic and political conditions. The hybrid method can be used as an appropriate method for estimating prices in many financial markets.

Suggested Citation

  • Parviz Sohrabi & Behshad Jodeiri Shokri & Hesam Dehghani, 2023. "Predicting coal price using time series methods and combination of radial basis function (RBF) neural network with time series," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(2), pages 207-216, June.
    • Handle: RePEc:spr:minecn:v:36:y:2023:i:2:d:10.1007_s13563-021-00286-z
    DOI: 10.1007/s13563-021-00286-z
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    References listed on IDEAS

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    1. Farmer, J. Doyne & Lafond, François, 2016. "How predictable is technological progress?," Research Policy, Elsevier, vol. 45(3), pages 647-665.
    2. Han, Meng & Ding, Lili & Zhao, Xin & Kang, Wanglin, 2019. "Forecasting carbon prices in the Shenzhen market, China: The role of mixed-frequency factors," Energy, Elsevier, vol. 171(C), pages 69-76.
    3. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
    4. Baomin Dong & Xuefeng Li & Boqiang Lin, 2010. "Forecasting Long‐Run Coal Price in China: A Shifting Trend Time‐Series Approach," Review of Development Economics, Wiley Blackwell, vol. 14(3), pages 499-519, August.
    5. Lance J. Bachmeier & James M. Griffin, 2006. "Testing for Market Integration: Crude Oil, Coal, and Natural Gas," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 55-72.
    6. Robert S. Pindyck, 1999. "The Long-Run Evolutions of Energy Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 1-27.
    7. Matyjaszek, Marta & Riesgo Fernández, Pedro & Krzemień, Alicja & Wodarski, Krzysztof & Fidalgo Valverde, Gregorio, 2019. "Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory," Resources Policy, Elsevier, vol. 61(C), pages 283-292.
    8. Cox, John C. & Ross, Stephen A. & Rubinstein, Mark, 1979. "Option pricing: A simplified approach," Journal of Financial Economics, Elsevier, vol. 7(3), pages 229-263, September.
    9. repec:bla:rdevec:v:14:y:2010:i:s1:p:499-519 is not listed on IDEAS
    10. Fan, Ying & Liang, Qiang & Wei, Yi-Ming, 2008. "A generalized pattern matching approach for multi-step prediction of crude oil price," Energy Economics, Elsevier, vol. 30(3), pages 889-904, May.
    Full references (including those not matched with items on IDEAS)

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