Coking coal futures price index forecasting with the neural network

Xu, Xiaojie; Zhang, Yun

doi:10.1007/s13563-022-00311-9

Brief Report
Published: 13 April 2022

Coking coal futures price index forecasting with the neural network

Xiaojie Xu¹ &
Yun Zhang¹

Mineral Economics (2022)Cite this article

112 Accesses
1 Citations
Metrics details

Abstract

Coking coal price forecasting is a significant issue for investors and policy makers. This study explores usefulness of the nonlinear autoregressive neural network for this forecasting problem in a dataset of daily closing prices of the coking coal futures traded in China Dalian Commodity Exchange during January 4, 2016–December 31, 2020. Through examining various model settings across the algorithm, delay, hidden neuron, and data splitting ratio, the model leading to generally accurate and stable performance is reached. Particularly, the model’s inputs are the lagged coking coal futures prices and output is the 1-day ahead price forecast. The model is based on the two-layer feedforward network with six delays and two hidden neurons, which is trained through the Levenberg-Marquardt algorithm, and leads to relative root mean square errors of 1.84%, 1.85%, and 1.84% for the training, validation, and testing phases, respectively. Usefulness of the machine learning technique for the price forecasting problem of the coking coal price is illustrated. Results here might be used on a standalone basis as technical forecasts or combined with fundamental forecasts to form perspectives of price trends and perform policy analysis.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

References

Al Bataineh A, Kaur D (2018) A comparative study of different curve fitting algorithms in artificial neural network using housing dataset. In: NAECON 2018-IEEE national aerospace and electronics conference, IEEE. pp 174–178
Alameer Z, Fathalla A, Li K, Ye H, Jianhua Z (2020) Multistep-ahead forecasting of coal prices using a hybrid deep learning model. Resources Policy 65:101588. https://doi.org/10.1016/j.resourpol.2020.101588
Article Google Scholar
Altan A, Karasu S, Zio E (2021) A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer. Appl Soft Comput 100:106996. https://doi.org/10.1016/j.asoc.2020.106996
Article Google Scholar
Awokuse TO, Yang J (2003) The informational role of commodity prices in formulating monetary policy: a reexamination. Economics Letters 79:219–224. https://doi.org/10.1016/S0165-1765(02)00331-2
Article Google Scholar
Babula RA, Bessler DA, Reeder J, Somwaru A (2004) Modeling us soy-based markets with directed acyclic graphs and bernanke structural var methods: the impacts of high soy meal and soybean prices. Journal of Food Distribution Research 35:29–52. https://doi.org/10.22004/ag.econ.27559
Article Google Scholar
Baghirli O (2015) Comparison of lavenberg-marquardt, scaled conjugate gradient and bayesian regularization backpropagation algorithms for multistep ahead wind speed forecasting using multilayer perceptron feedforward neural network. https://www.diva-portal.org/smash/get/diva2:828170/FULLTEXT01.pdf
Batra D (2014) Comparison between levenberg-marquardt and scaled conjugate gradient training algorithms for image compression using mlp. Int J Image Process (IJIP) 8:412–422
Google Scholar
Benalcazar P, Krawczyk M, Kamiński J (2017) Forecasting global coal consumption: an artificial neural network approach. Gospodarka Surowcami Mineralnymi 33
Bessler DA (1982) Adaptive expectations, the exponentially weighted forecast, and optimal statistical predictors: a revisit. Agric Econ Res 34:16–23. https://doi.org/10.22004/ag.econ.148819
Article Google Scholar
Bessler DA (1990) Forecasting multiple time series with little prior information. Am J Agric Econ 72:788–792. https://doi.org/10.2307/1243059
Article Google Scholar
Bessler DA, Babula RA (1987) Forecasting wheat exports: Do exchange rates matter?. Journal of Business & Economic Statistics 5:397–406. https://doi.org/10.2307/1391615
Article Google Scholar
Bessler DA, Brandt JA (1981) Forecasting livestock prices with individual and composite methods. Appl Econ 13:513–522. https://doi.org/10.1080/00036848100000016
Article Google Scholar
Bessler DA, Brandt JA (1992) An analysis of forecasts of livestock prices. Journal of Economic Behavior & Organization 18:249–263. https://doi.org/10.1016/0167-2681(92)90030-F
Article Google Scholar
Bessler DA, Chamberlain PJ (1988) Composite forecasting with dirichlet priors. Decis Sci 19:771–781. https://doi.org/10.1111/j.1540-5915.1988.tb00302.x
Article Google Scholar
Bessler DA, Hopkins JC (1986) Forecasting an agricultural system with random walk priors. Agric Syst 21:59–67. https://doi.org/10.1016/0308-521X(86)90029-6
Article Google Scholar
Bessler DA, Kling JL (1986) Forecasting vector autoregressions with bayesian priors. Am J Agric Econ 68:144–151. https://doi.org/10.2307/1241659
Article Google Scholar
Bessler DA, Wang Z (2012) D-separation, forecasting, and economic science: a conjecture. Theor Decis 73:295–314. https://doi.org/10.1007/s11238-012-9305-8
Article Google Scholar
Bessler DA, Yang J, Wongcharupan M (2003) Price dynamics in the international wheat market: modeling with error correction and directed acyclic graphs. J Reg Sci 43:1–33. https://doi.org/10.1111/1467-9787.00287
Article Google Scholar
Brandt JA, Bessler DA (1981) Composite forecasting: an application with us hog prices. Am J Agric Econ 63:135–140. https://doi.org/10.2307/1239819
Article Google Scholar
Brandt JA, Bessler DA (1982) Forecasting with a dynamic regression model: a heuristic approach. North Central Journal of Agricultural Economics 4:27–33. https://doi.org/10.2307/1349096
Article Google Scholar
Brandt JA, Bessler DA (1983) Price forecasting and evaluation: An application in agriculture. J Forecast 2:237–248. https://doi.org/10.1002/for.3980020306
Article Google Scholar
Brandt JA, Bessler DA (1984) Forecasting with vector autoregressions versus a univariate arima process: an empirical example with us hog prices. North Central Journal of Agricultural Economics 4:29–36. https://doi.org/10.2307/1349248
Article Google Scholar
Broock WA, Scheinkman JA, Dechert WD, LeBaron B (1996) A test for independence based on the correlation dimension. Econometric Reviews 15:197–235. https://doi.org/10.1080/07474939608800353
Article Google Scholar
Chan HL, Lee SK (1997) Modelling and forecasting the demand for coal in China. Energy Economics 19:271–287. https://doi.org/10.1016/S0140-9883(96)01019-5
Article Google Scholar
Chen DT, Bessler DA (1987) Forecasting the us cotton industry: Structural and time series approaches. In: Proceedings of the NCR-134 conference on applied commodity price analysis. Forecasting and Market Risk Management, Chicago Mercantile Exchange, Chicago
Chen DT, Bessler DA (1990) Forecasting monthly cotton price: Structural and time series approaches. Int J Forecast 6:103–113. https://doi.org/10.1016/0169-2070(90)90101-G
Article Google Scholar
Dergiades T, Martinopoulos G, Tsoulfidis L (2013) Energy consumption and economic growth: parametric and non-parametric causality testing for the case of greece. Energy Economics 36:686–697. https://doi.org/10.1016/j.eneco.2012.11.017
Article Google Scholar
Ding L, Zhao Z, Han M (2021) Probability density forecasts for steam coal prices in China: the role of high-frequency factors. Energy 220:119758. https://doi.org/10.1016/j.energy.2021.119758
Article Google Scholar
Doan CD, Liong SY (2004) Generalization for multilayer neural network Bayesian regularization or early stopping. In: Proceedings of Asia pacific association of hydrology and water resources 2nd conference, pp 5–8
Dong B, Li X, Lin B (2010) Forecasting long-run coal price in China: a shifting trend time-series approach. Rev Dev Econ 14:499–519. https://doi.org/10.1111/j.1467-9361.2010.00567.x
Article Google Scholar
Drachal K (2018) Comparison between Bayesian and information-theoretic model averaging: fossil fuels prices example. Energy Economics 74:208–251. https://doi.org/10.1016/j.eneco.2018.04.043
Article Google Scholar
Fan X, Wang L, Li S (2016) Predicting chaotic coal prices using a multi-layer perceptron network model. Resources Policy 50:86–92. https://doi.org/10.1016/j.resourpol.2016.08.009
Article Google Scholar
Fujihara RA, Mougoué M (1997) An examination of linear and nonlinear causal relationships between price variability and volume in petroleum futures markets. Journal of Futures Markets: Futures, Options, and Other Derivative Products 17:385–416. https://doi.org/10.1002/(SICI)1096-9934(199706)17:4<385::AID-FUT2>3.0.CO;2-D
Article Google Scholar
Gao L, Wang G (2021) Research on the improved neural network of coal price forecast based on co-integration theory. In: IOP conference series: Earth and environmental science, IOP Publishing. pp 042028
Guo X, Shi J, Ren D (2016) Coal price forecasting and structural analysis in china. Discret Dyn Nat Soc
Hagan MT, Menhaj MB (1994) Training feedforward networks with the marquardt algorithm. IEEE Trans Neural Netw 5:989–993. https://doi.org/10.1109/72.329697
Article Google Scholar
Jaipuria S (2019) Prediction of LAM coke price using ANN and ANFIS model. International Journal of Applied Research in Management and Economics 2:7–17. https://doi.org/10.33422/ijarme.v2i3.267
Article Google Scholar
Jiang S, Yang C, Guo J, Ding Z (2018) Arima forecasting of china’s coal consumption, price and investment by 2030. Energy Sources, Part B: Economics, Planning, and Policy 13:190–195. https://doi.org/10.1080/15567249.2017.1423413
Article Google Scholar
Kano Y, Shimizu S et al (2003) Causal inference using nonnormality. In: Proceedings of the international symposium on science of modeling, the 30th anniversary of the information criterion, pp 261–270, http://www.ar.sanken.osaka-u.ac.jp/sshimizu/papers/aic30_web2.pdf
Karasu S, Altan A, Bekiros S, Ahmad W (2020) A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series. Energy 212:118750. https://doi.org/10.1016/j.energy.2020.118750
Article Google Scholar
Karasu S, Altan A, Saraç Z, Hacioğlu R (2017a) Estimation of fast varied wind speed based on narx neural network by using curve fitting. Int J Energy Applic Technol 4:137–146. https://dergipark.org.tr/en/download/article-file/354536
Google Scholar
Karasu S, Altan A, Saraç Z, Hacioğlu R (2017b) Prediction of wind speed with non-linear autoregressive (nar) neural networks. In: 2017 25Th signal processing and communications applications conference (SIU), IEEE, pp 1–4
Kayri M (2016) Predictive abilities of bayesian regularization and Levenberg–Marquardt algorithms in artificial neural networks: a comparative empirical study on social data. Mathematical and Computational Applications 21:20. https://doi.org/10.3390/mca21020020
Article Google Scholar
Khan TA, Alam M, Shahid Z, Mazliham M (2019) Comparative performance analysis of Levenberg-Marquardt, Bayesian regularization and scaled conjugate gradient for the prediction of flash floods. Journal of Information Communication Technologies and Robotic Applications 10:52–58. http://jictra.com.pk/index.php/jictra/article/view/188/112
Google Scholar
Kim YJ, Kang HJ (2016) Development of a forecast model for thermal coal price. Journal of Service Research and Studies 6:75–85. https://doi.org/10.18807/jsrs.2016.6.4.075
Article Google Scholar
Kling JL, Bessler DA (1985) A comparison of multivariate forecasting procedures for economic time series. Int J Forecast 1:5–24. https://doi.org/10.1016/S0169-2070(85)80067-4
Article Google Scholar
Krzemień A, Fernández PR, Sánchez AS, Lasheras FS (2015) Forecasting european thermal coal spot prices. Journal of Sustainable Mining 14:203–210. https://doi.org/10.1016/j.jsm.2016.04.002
Article Google Scholar
Levenberg K (1944) A method for the solution of certain non-linear problems in least squares. Q Appl Math 2:164–168. https://doi.org/10.1090/qam/10666
Article Google Scholar
Li L (2021) Prediction of coal prices based on random forest and lasso regression. Int Core J Eng 7:67–73. https://doi.org/10.6919/ICJE.202109_7(9).0011
Article Google Scholar
LIAO Zw, CHEN Lt, HUANG Jd, ZHUANG J (2021) Medium and short-term electricity coal price forecast based on feature space transformation and lstm. Journal of Northeastern University (Natural Science) 42:483. https://doi.org/10.12068/j.issn.1005-3026.2021.04.005
Article Google Scholar
Lin Bq, Liu Jh (2010) Estimating coal production peak and trends of coal imports in China. Energy Policy 38:512–519. https://doi.org/10.1016/j.enpol.2009.09.042
Article Google Scholar
Liu X (2021) Research on the forecast of coal price based on LSTM with improved Adam optimizer. In: Journal of Physics: Conference Series, IOP Publishing, pp 012069, https://doi.org/10.1088/1742-6596/1941/1/012069.
Manowska A (2020) Using the lstm network to forecast the demand for hard coal. gospodarka surowcami mineralnymi 36. https://doi.org/10.24425/gsm.2020.133945
Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11:431–441. https://doi.org/10.1137/0111030
Article Google Scholar
Martinho AD, Goliatt L, Hippert H (2020) Gaussian process models applied for monthly forecast coal price mineral: a case study of mozambique. In: Proceedings of the XLI Ibero-Latin-American Congress on Computational Methods in Engineering, ABMEC
Matyjaszek M, Fernández PR, Krzemień A, Wodarski K, Valverde GF (2019) Forecasting coking coal prices by means of arima models and neural networks, considering the transgenic time series theory. Resources Policy 61:283–292. https://doi.org/10.1016/j.resourpol.2019.02.017
Article Google Scholar
Matyjaszek M, Fidalgo Valverde G, Krzemień A, Wodarski K, Riesgo Fernández P (2020) Optimizing predictor variables in artificial neural networks when forecasting raw material prices for energy production. Energies 13:2017. https://doi.org/10.3390/en13082017
Article Google Scholar
Matyjaszek M, Wodarski K, Krzemień A, García-Miranda CE, Sánchez AS (2018) Coking coal mining investment: boosting european union’s raw materials initiative. Resources Policy 57:88–97. https://doi.org/10.1016/j.resourpol.2018.01.012
Article Google Scholar
McIntosh CS, Bessler DA (1988) Forecasting agricultural prices using a bayesian composite approach. J Agric Appl Econ 20:73–80. https://doi.org/10.1017/S0081305200017611
Article Google Scholar
Meifeng Z, Guohao Z (2015) Forecasting the coke price based on the Kalman filtering algorithm. Journal of Resources and Ecology 6:60–64. https://doi.org/10.5814/j.issn.1674-764x.2015.01.008
Article Google Scholar
Møller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:525–533. https://doi.org/10.1016/S0893-6080(05)80056-5
Article Google Scholar
Ozga-Blaschke U (2018) Coking coal prices on the international market–the current situation and forecasts. Zeszyty Naukowe Instytutu Gospodarki Surowcami Mineralnymi Polskiej Akademii Nauk 105:53–62. https://doi.org/10.24425/124388
Article Google Scholar
Paluszek M, Thomas S (2020) Practical MATLAB deep learning: a project-based approach. Apress. https://link.springer.com/content/pdf/10.1007/978-1-4842-5124-9.pdf
Selvamuthu D, Kumar V, Mishra A (2019) Indian stock market prediction using artificial neural networks on tick data. Financial Innovation 5:16. https://doi.org/10.1186/s40854-019-0131-7
Article Google Scholar
Shafiee S, Topal E (2010) A long-term view of worldwide fossil fuel prices. Appl Energy 87:988–1000. https://doi.org/10.1016/j.apenergy.2009.09.012
Article Google Scholar
Shimizu S, Hoyer PO, Hyvärinen A, Kerminen A, Jordan M (2006) A linear non-gaussian acyclic model for causal discovery. J Mach Learn Res 7:2003–2030. https://www.jmlr.org/papers/volume7/shimizu06a/shimizu06a.pdf?ref=https://codemonkey.link
Google Scholar
Shimizu S, Inazumi T, Sogawa Y, Hyvärinen A., Kawahara Y, Washio T, Hoyer PO, Bollen K (2011) Directlingam: a direct method for learning a linear non-gaussian structural equation model. J Mach Learn Res 12:1225–1248. https://www.jmlr.org/papers/volume12/shimizu11a/shimizu11a.pdf
Google Scholar
Shimizu S, Kano Y (2008) Use of non-normality in structural equation modeling: application to direction of causation. Journal of Statistical Planning and Inference 138:3483–3491. https://doi.org/10.1016/j.jspi.2006.01.017
Article Google Scholar
Sohrabi P, Jodeiri Shokri B, Dehghani H (2021) Predicting coal price using time series methods and combination of radial basis function (rbf) neural network with time series. Mineral Economics, pp 1–10
Wang C, Kang W (2021) Forecasting china’s steam coal prices using dynamic factors and mixed-frequency data. Pol J Environ Stud 30:4241–4254. https://doi.org/10.15244/pjoes/131856
Article Google Scholar
Wang J, Dong Y, Wu J, Mu R, Jiang H (2011) Coal production forecast and low carbon policies in china. Energy Policy 39:5970–5979. https://doi.org/10.1016/j.enpol.2011.06.058
Article Google Scholar
Wang T, Yang J (2010) Nonlinearity and intraday efficiency tests on energy futures markets. Energy Economics 32:496–503. https://doi.org/10.1016/j.eneco.2009.08.001
Article Google Scholar
Wang Z, Bessler DA (2004) Forecasting performance of multivariate time series models with full and reduced rank: An empirical examination. Int J Forecast 20:683–695. https://doi.org/10.1016/j.ijforecast.2004.01.002
Article Google Scholar
Wegener C, von Spreckelsen C, Basse T, von Mettenheim HJ (2016) Forecasting government bond yields with neural networks considering cointegration. J Forecast 35:86–92. https://doi.org/10.1002/for.2385
Article Google Scholar
Xu X (2015) Cointegration among regional corn cash prices. Econ Bull 35:2581–2594. http://www.accessecon.com/Pubs/EB/2015/Volume35/EB-15-V35-I4-P259.pdf
Google Scholar
Xu X (2017a) Contemporaneous causal orderings of us corn cash prices through directed acyclic graphs. Empir Econ 52:731–758. https://doi.org/10.1007/s00181-016-1094-4
Article Google Scholar
Xu X (2017b) The rolling causal structure between the chinese stock index and futures. Fin Mkts Portfolio Mgmt 31:491–509. https://doi.org/10.1007/s11408-017-0299-7
Article Google Scholar
Xu X (2018a) Causal structure among us corn futures and regional cash prices in the time and frequency domain. J Appl Stat 45:2455–2480. https://doi.org/10.1080/02664763.2017.1423044
Article Google Scholar
Xu X (2018b) Cointegration and price discovery in us corn cash and futures markets. Empir Econ 55:1889–1923. https://doi.org/10.1007/s00181-017-1322-6
Article Google Scholar
Xu X (2018c) Intraday price information flows between the csi300 and futures market: an application of wavelet analysis. Empirical Economics 54:1267–1295. https://doi.org/10.1007/s00181-017-1245-2
Article Google Scholar
Xu X (2018d) Linear and nonlinear causality between corn cash and futures prices. Journal of Agricultural & Food Industrial Organization 16:20160006. https://doi.org/10.1515/jafio-2016-0006
Article Google Scholar
Xu X (2019a) Contemporaneous and granger causality among us corn cash and futures prices. Eur Rev Agric Econ 46:663–695. https://doi.org/10.1093/erae/jby036
Article Google Scholar
Xu X (2019b) Contemporaneous causal orderings of csi300 and futures prices through directed acyclic graphs. Econ Bull 39:2052–2077. http://www.accessecon.com/Pubs/EB/2019/Volume39/EB-19-V39-I3-P192.pdf
Google Scholar
Xu X (2019c) Price dynamics in corn cash and futures markets: cointegration, causality, and forecasting through a rolling window approach. Financial Markets and Portfolio Management 33:155–181. https://doi.org/10.1007/s11408-019-00330-7.
Article Google Scholar
Xu X (2020a) Corn cash price forecasting. Am J Agric Econ 102:1297–1320. https://doi.org/10.1002/ajae.12041
Article Google Scholar
Xu X, Zhang Y (2021a) Corn cash price forecasting with neural networks. Comput Electron Agric 184:106120. https://doi.org/10.1016/j.compag.2021.106120
Article Google Scholar
Xu X, Zhang Y (2021b) House price forecasting with neural networks. Intell Syst Applic 12:200052. https://doi.org/10.1016/j.iswa.2021.200052
Article Google Scholar
Xu X, Zhang Y (2021c) Individual time series and composite forecasting of the chinese stock index. Mach Learn Applic 5:100035. https://doi.org/10.1016/j.mlwa.2021.100035
Article Google Scholar
Xu X, Zhang Y (2021d) Network analysis of corn cash price comovements. Mach Learn Applic 6:100140. https://doi.org/10.1016/j.mlwa.2021.100140
Article Google Scholar
Xu X, Zhang Y (2021e) Rent index forecasting through neural networks. Journal of Economic Studies
Xu X, Zhang Y (2021f) Second-hand house price index forecasting with neural networks. Journal of Property Research
Xu X, Zhang Y (2022a) Contemporaneous causality among one hundred Chinese cities. Empirical Economics
Xu X, Zhang Y (2022b) Network analysis of housing price comovements of a hundred chinese cities. National Institute Economic Review
Xu Y (2020b) Forecast of coal price based on random forest and lasso regression. Frontiers in Economics and Management 1:260–266. https://doi.org/10.6981/FEM.202011_1(11).0036
Article Google Scholar
Yang J, Awokuse TO (2003) Asset storability and hedging effectiveness in commodity futures markets. Appl Econ Lett 10:487–491. https://doi.org/10.1080/1350485032000095366
Article Google Scholar
Yang J, Cabrera J, Wang T (2010) Nonlinearity, data-snooping, and stock index ETF return predictability. Eur J Oper Res 200:498–507. https://doi.org/10.1016/j.ejor.2009.01.009
Article Google Scholar
Yang J, Haigh MS, Leatham DJ (2001) Agricultural liberalization policy and commodity price volatility: a GARCH application. Appl Econ Lett 8:593–598. https://doi.org/10.1080/13504850010018734
Article Google Scholar
Yang J, Leatham DJ (1998) Market efficiency of us grain markets: application of cointegration tests. Agribusiness: An International Journal 14:107–112. https://doi.org/10.1002/(SICI)1520-6297(199803/04)14:2<107::AID-AGR3>3.0.CO;2-6
Article Google Scholar
Yang J, Li Z, Wang T (2021) Price discovery in chinese agricultural futures markets: a comprehensive look. J Futur Mark 41:536–555. https://doi.org/10.1002/fut.22179
Article Google Scholar
Yang J, Su X, Kolari JW (2008) Do euro exchange rates follow a martingale? some out-of-sample evidence. Journal of Banking & Finance 32:729–740. https://doi.org/10.1016/j.jbankfin.2007.05.009
Article Google Scholar
Yang J, Zhang J, Leatham DJ (2003) Price and volatility transmission in international wheat futures markets. Annals of Economics and Finance 4:37–50. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.295.2182&rep=rep1&type=pdf
Google Scholar
Yawei O, Li O (2011) A forecast of coal demand based on the simultaneous equations model. In: 2011 IEEE international symposium on IT in medicine and education, IEEE, pp 677–679
Yu-zhao F, Hong-wei Z, Yi C, Li-qiang T, Ping W (2009) Price forecasting algorithm for coal and electricity based on pso and rbf neural network. In: 2009 IEEE international conference on control and automation, IEEE, pp 1365–1369
Yun C, Kai Z, Jing L (2014) Study On forecast model of crcp index in china. In: 2014 11Th international conference on service systems and service management (ICSSSM), IEEE, pp 1–3
Zhang K, Cao H, Thé J, Yu H (2022) A hybrid model for multi-step coal price forecasting using decomposition technique and deep learning algorithms. Appl Energy 306:118011. https://doi.org/10.1016/j.apenergy.2021.118011
Article Google Scholar
Zhang X, Liu C, Qian Y (2020) Coal price forecast based on arima model. Financial Forum, 180–184
Zhou HD, Wu Q, Fang M, Ren ZB, Jin LF (2013) Prediction of demand trends of coking coal in China based on grey linear regression composition model. International Journal of Global Energy Issues 36:197–209. https://doi.org/10.1504/IJGEI.2013.061794
Article Google Scholar
Zhu Q, Zhang Z, Li R, Lai KK, Wang S, Chai J (2014) Structural analysis and total coal demand forecast in china. Discret Dyn Nat Soc 2014:612064. https://doi.org/10.1155/2014/612064
Article Google Scholar
Zou S, Ma T, Feng T (2018) The forecasting of china’s coal futures volatility based on garch models. Int J Sci 5:246–253. http://www.ijscience.org/download/IJS-5-12-246-253.pdf
Google Scholar

Download references

Author information

Authors and Affiliations

North Carolina State University, Raleigh, NC, 27695, USA
Xiaojie Xu & Yun Zhang

Authors

Xiaojie Xu
View author publications
You can also search for this author in PubMed Google Scholar
Yun Zhang
View author publications
You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaojie Xu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Xu, X., Zhang, Y. Coking coal futures price index forecasting with the neural network. Miner Econ (2022). https://doi.org/10.1007/s13563-022-00311-9

Download citation

Received: 18 December 2021
Accepted: 05 March 2022
Published: 13 April 2022
DOI: https://doi.org/10.1007/s13563-022-00311-9

Keywords

Coking coal
Price forecasting
Time series
Neural network
Machine learning