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Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory

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  • Matyjaszek, Marta
  • Riesgo Fernández, Pedro
  • Krzemień, Alicja
  • Wodarski, Krzysztof
  • Fidalgo Valverde, Gregorio

Abstract

Price forecasting is a vital matter for mining investment decisions, as it represents the credibility of any financial outcome claimed by the feasibility studies presented to investors in financial markets. Most of these financial studies use forecasts from well-known providers of price assessments and market data that, ultimately, constitute a black box for the investors. This is why, to achieve credibility, user-friendly forecasting techniques through which the future price instability can be bounded are needed.

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  • 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.
    • Handle: RePEc:eee:jrpoli:v:61:y:2019:i:c:p:283-292
    DOI: 10.1016/j.resourpol.2019.02.017
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    as
    1. Antonio Bernardo Sánchez & Celestino Ordóñez & Fernando Sánchez Lasheras & Francisco Javier de Cos Juez & Javier Roca-Pardiñas, 2013. "Forecasting SO 2 Pollution Incidents by means of Elman Artificial Neural Networks and ARIMA Models," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-6, December.
    2. Dooley, Gillian & Lenihan, Helena, 2005. "An assessment of time series methods in metal price forecasting," Resources Policy, Elsevier, vol. 30(3), pages 208-217, September.
    3. Mills,Terence C. & Markellos,Raphael N., 2008. "The Econometric Modelling of Financial Time Series," Cambridge Books, Cambridge University Press, number 9780521710091.
    4. Sánchez Lasheras, Fernando & de Cos Juez, Francisco Javier & Suárez Sánchez, Ana & Krzemień, Alicja & Riesgo Fernández, Pedro, 2015. "Forecasting the COMEX copper spot price by means of neural networks and ARIMA models," Resources Policy, Elsevier, vol. 45(C), pages 37-43.
    5. Labys, W C & Lesourd, J B & Badillo, D, 1998. "The existence of metal price cycles," Resources Policy, Elsevier, vol. 24(3), pages 147-155, September.
    6. Bento, P.M.R. & Pombo, J.A.N. & Calado, M.R.A. & Mariano, S.J.P.S., 2018. "A bat optimized neural network and wavelet transform approach for short-term price forecasting," Applied Energy, Elsevier, vol. 210(C), pages 88-97.
    7. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    8. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    9. Weron, Rafal & Misiorek, Adam, 2008. "Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 744-763.
    10. Nawrocki, Tomasz Leszek & Jonek-Kowalska, Izabela, 2016. "Assessing operational risk in coal mining enterprises – Internal, industrial and international perspectives," Resources Policy, Elsevier, vol. 48(C), pages 50-67.
    11. Sawa, Takamitsu, 1978. "Information Criteria for Discriminating among Alternative Regression Models," Econometrica, Econometric Society, vol. 46(6), pages 1273-1291, November.
    12. Crespo Cuaresma, Jesús & Hlouskova, Jaroslava & Kossmeier, Stephan & Obersteiner, Michael, 2004. "Forecasting electricity spot-prices using linear univariate time-series models," Applied Energy, Elsevier, vol. 77(1), pages 87-106, January.
    13. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    14. He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2017. "Price forecasting in the precious metal market: A multivariate EMD denoising approach," Resources Policy, Elsevier, vol. 54(C), pages 9-24.
    15. Riesgo García, María Victoria & Krzemień, Alicja & Manzanedo del Campo, Miguel Ángel & Escanciano García-Miranda, Carmen & Sánchez Lasheras, Fernando, 2018. "Rare earth elements price forecasting by means of transgenic time series developed with ARIMA models," Resources Policy, Elsevier, vol. 59(C), pages 95-102.
    16. Krzemień, Alicja & Riesgo Fernández, Pedro & Suárez Sánchez, Ana & Diego Álvarez, Isidro, 2016. "Beyond the pan-european standard for reporting of exploration results, mineral resources and reserves," Resources Policy, Elsevier, vol. 49(C), pages 81-91.
    17. Matyjaszek, Marta & Wodarski, Krzysztof & Krzemień, Alicja & Escanciano García-Miranda, Carmen & Suárez Sánchez, Ana, 2018. "Coking coal mining investment: Boosting European Union's raw materials initiative," Resources Policy, Elsevier, vol. 57(C), pages 88-97.
    18. Mills,Terence C. & Markellos,Raphael N., 2008. "The Econometric Modelling of Financial Time Series," Cambridge Books, Cambridge University Press, number 9780521883818.
    19. Kriechbaumer, Thomas & Angus, Andrew & Parsons, David & Rivas Casado, Monica, 2014. "An improved wavelet–ARIMA approach for forecasting metal prices," Resources Policy, Elsevier, vol. 39(C), pages 32-41.
    20. Vaněk, Michal & Bora, Petr & Maruszewska, Ewa Wanda & Kašparková, Alena, 2017. "Benchmarking of mining companies extracting hard coal in the Upper Silesian Coal Basin," Resources Policy, Elsevier, vol. 53(C), pages 378-383.
    21. Fereshteh Modaresi & Shahab Araghinejad & Kumars Ebrahimi, 2018. "A Comparative Assessment of Artificial Neural Network, Generalized Regression Neural Network, Least-Square Support Vector Regression, and K-Nearest Neighbor Regression for Monthly Streamflow Forecasti," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 243-258, January.
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    23. Guangyong Zhang & Lixin Tian & Min Fu & Bingyue Wan & Wenbin Zhang, 2020. "Research on the Transmission Ability of China’s Thermal Coal Price Information Based on Directed Limited Penetrable Interdependent Network," Sustainability, MDPI, vol. 12(18), pages 1-23, September.

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