Scrap steel price forecasting with neural networks for east, north, south, central, northeast, and southwest China and at the national level

References

• Foldes L. Iron and steel prices. Economica. 1956;23(92):344–356. doi:10.2307/2551460
   Web of Science ®Google Scholar
• Li B, Ding J, Yin Z, et al. Optimized neural network combined model based on the induced ordered weighted averaging operator for vegetable price forecasting. Expert Syst Appl. 2021;168:114232. doi:10.1016/j.eswa.2020.114232
   Web of Science ®Google Scholar
• Xu X. Short-run price forecast performance of individual and composite models for 496 corn cash markets. J Appl Stat. 2017;44(14):2593–2620. 10.1080/02664763.2016.1259399
   Web of Science ®Google Scholar
• Cooney S. Steel: price and policy issues. 2007.
   Google Scholar
• Drachal K, Pawłowski M. A review of the applications of genetic algorithms to forecasting prices of commodities. Economies. 2021;9(1):6. doi:10.3390/economies9010006
   Web of Science ®Google Scholar
• Xu X. Using local information to improve short-run corn price forecasts. J Agric Food Ind Organ. 2018;16:20170018. doi:10.1515/jafio-2017-0018.
   Google Scholar
• Yin H, Jin D, Gu YH, et al. Stl-attlstm vegetable price forecasting using stl and attention mechanism-based lstm. Agriculture. 2020;10(12):612. doi:10.3390/agriculture10120612
   Web of Science ®Google Scholar
• Robles M, Torero M, Von Braun J. When speculation matters. Technical Report. 2009.
   Google Scholar
• Xu X, Zhang Y. An integrated vector error correction and directed acyclic graph method for investigating contemporaneous causalities. Decis Anal J. 2023;7:100229. doi:10.1016/j.dajour.2023.100229
   Google Scholar
• Chapoto A, Jayne TS. The impacts of trade barriers and market interventions on maize price predictability: evidence from Eastern and Southern Africa. Technical Report. 2009. doi:10.22004/ag.econ.56798
   Google Scholar
• Milford RL, Pauliuk S, Allwood JM, et al. The roles of energy and material efficiency in meeting steel industry CO2 targets. Environ Sci Technol. 2013;47(7):3455–3462. doi:10.1021/es3031424
   PubMed Web of Science ®Google Scholar
• Xu X, Zhang Y. Network analysis of price comovements among corn futures and cash prices. J Agric Food Ind Organ. 2022. doi:10.1515/jafio-2022-0009
   Google Scholar
• Dacha K, Cherukupalli R, Sinha A. Food index forecasting, in: Applied Advanced Analytics. Springer; 2021. p. 125–134. doi:10.1007/978-981-33-6656-5_11
   Google Scholar
• Forestal RL, Pi S-M. Using artificial neural networks and optimal scaling model to forecast agriculture commodity price: an ecological-economic approach. Adv Manag Appl Econ. 2021;11:29–55.
   Google Scholar
• Ilmark S. Market forecast for stainless steel. Metallurgist. 2013;56(11-12):883–887. doi:10.1007/s11015-013-9668-2
   Web of Science ®Google Scholar
• Zhou K, Yang S. Emission reduction of China's steel industry: progress and challenges. Renew Sustain Energy Rev. 2016;61:319–327. doi:10.1016/j.rser.2016.04.009
   Web of Science ®Google Scholar
• Xuan Y, Yue Q. Forecast of steel demand and the availability of depreciated steel scrap in China. Resour Conserv Recycl. 2016;109:1–12. doi:10.1016/j.resconrec.2016.02.003
   Web of Science ®Google Scholar
• Xu X, Zhang Y. Steel price index forecasting through neural networks: the composite index, long products, flat products, and rolled products. Min Econ. 2022;1–20. doi:10.1007/s13563-022-00357-9
   Web of Science ®Google Scholar
• Yin X, Chen W. Trends and development of steel demand in China: a bottom–up analysis. Resour Policy. 2013;38(4):407–415. doi:10.1016/j.resourpol.2013.06.007
   Web of Science ®Google Scholar
• Lin B, Wang X. Exploring energy efficiency in China's iron and steel industry: a stochastic frontier approach. Energy Policy. 2014;72:87–96. doi:10.1016/j.enpol.2014.04.043
   Web of Science ®Google Scholar
• Song L, Wang P, Hao M, et al. Mapping provincial steel stocks and flows in China: 1978–2050. J Clean Prod. 2020;262:121393. doi:10.1016/j.jclepro.2020.121393
   Web of Science ®Google Scholar
• Wang M, Tian Y, Liang Y, et al. Forecast scrap generation and emission reduction of China's steel industry. In: International Conference on Management Science and Engineering Management, Springer; 2019. p. 283–292. doi:10.1007/978-3-030-21248-3_21
   Google Scholar
• Liu Y, Li H, Guan J, et al. Influence of different factors on prices of upstream, middle and downstream products in China's whole steel industry chain: based on adaptive neural fuzzy inference system. Resour Policy. 2019;60:134–142. doi:10.1016/j.resourpol.2018.12.009
   Web of Science ®Google Scholar
• Mele M, Magazzino C. A machine learning analysis of the relationship among iron and steel industries, air pollution, and economic growth in China. J Cleaner Prod. 2020;277:123293.
   Web of Science ®Google Scholar
• Kim K, Lim S. Price discovery and volatility spillover in spot and futures markets: evidences from steel-related commodities in China. Appl Econ Lett. 2019;26(5):351–357. doi:10.1080/13504851.2018.1478385
   Web of Science ®Google Scholar
• Arık E, Mutlu E. Chinese steel market in the post-futures period. Resour Policy. 2014;42:10–17.
   Web of Science ®Google Scholar
• Indriawan I, Liu Q, Tse Y. Market quality and the connectedness of steel rebar and other industrial metal futures in China. J Futures Mark. 2019;39(11):1383–1393. doi:10.1002/fut.22001
   Web of Science ®Google Scholar
• Morfeldt J, Nijs W, Silveira S. The impact of climate targets on future steel production–an analysis based on a global energy system model. J Clean Prod. 2015;103:469–482. doi:10.1016/j.jclepro.2014.04.045
   Web of Science ®Google Scholar
• Labson BS. Changing patterns of trade in the world iron ore and steel market: an econometric analysis. J Policy Model. 1997;19(3):237–251. doi:10.1016/S0161-8938(96)00049-X
   Web of Science ®Google Scholar
• Guo Z, Fu Z. Current situation of energy consumption and measures taken for energy saving in the iron and steel industry in China. Energy. 2010;35(11):4356–4360. doi:10.1016/j.energy.2009.04.008
   Web of Science ®Google Scholar
• Lin B, Wang X. Carbon emissions from energy intensive industry in China: evidence from the iron & steel industry. Renew Sust Energ Rev. 2015;47:746–754. doi:10.1016/j.rser.2015.03.056
   Web of Science ®Google Scholar
• Lin B, Wu Y, Zhang L. Estimates of the potential for energy conservation in the Chinese steel industry. Energy Policy. 2011;39(6):3680–3689. doi:10.1016/j.enpol.2011.03.077
   Web of Science ®Google Scholar
• Wang T, Tian X, Hashimoto S, et al. Concrete transformation of buildings in China and implications for the steel cycle. Resour Conserv Recycl. 2015;103:205–215. doi:10.1016/j.resconrec.2015.07.021
   Web of Science ®Google Scholar
• Chan S, Han G, Zhang W. How strong are the linkages between real estate and other sectors in China?Res Int Bus Finance. 2016;36:52–72. doi:10.1016/j.ribaf.2015.09.018
   Web of Science ®Google Scholar
• Zhu X, Lin S, Wang L, et al. A study of the debt of real estate-related industries. In: A study of the turning point of China's debt, Springer; 2018. p. 123–163. doi:10.1007/978-981-13-1325-7_6
   Google Scholar
• Wang P, Li W, Kara S. Cradle-to-cradle modeling of the future steel flow in China. Res Conserv Recycl. 2017;117:45–57. doi:10.1016/j.resconrec.2015.07.009
   Web of Science ®Google Scholar
• Hu M, Pauliuk S, Wang T, et al. Iron and steel in Chinese residential buildings: a dynamic analysis. Resour Conserv Recycl. 2010;54(9):591–600. doi:10.1016/j.resconrec.2009.10.016
   Web of Science ®Google Scholar
• Hasanbeigi A, Jiang Z, Price L. Retrospective and prospective analysis of the trends of energy use in Chinese iron and steel industry. J Clean Prod. 2014;74:105–118. doi:10.1016/j.jclepro.2014.03.065
   Web of Science ®Google Scholar
• Andrikopoulos A, Merika A, Merikas A, et al. The dynamics of fleet size and shipping profitability: the role of steel-scrap prices. Marit Policy Manag. 2020;47(8):985–1009. doi:10.1080/03088839.2020.1735007
   Web of Science ®Google Scholar
• Benjamin C, Houee-Bigot M, Tavera C. What are the long-term drivers of food prices? Investigating improvements in the accuracy of prediction intervals for the forecast of food prices. Technical Report, 2009. doi:10.22004/ag.econ.49436
   Google Scholar
• Negri P, Ramos P, Breitkopf M. Regional commodities price volatility assessment using self-driven recurrent networks. In: Iberoamerican Congress on Pattern Recognition, Springer; 2021. p. 361–370. doi:10.1007/978-3-030-93420-0_34
   Google Scholar
• Serra T, Gil JM. Price volatility in food markets: can stock building mitigate price fluctuations? Eur Rev Agric Econ. 2013;40(3):507–528. doi:10.1093/erae/jbs041
   Web of Science ®Google Scholar
• Xu X. Contemporaneous causal orderings of us corn cash prices through directed acyclic graphs. Empir Econ. 2017;52(2):731–758. doi:10.1007/s00181-016-1094-4
   Web of Science ®Google Scholar
• Yeasin M, Singh K, Lama A, et al. Modelling volatility influenced by exogenous factors using an improved garch-x model. J Ind Soc Agric Stat. 2020;74:209–216.
   Google Scholar
• Xu X. Corn cash price forecasting. Am J Agric Econ. 2020;102(4):1297–1320. doi:10.1002/ajae.12041
   Web of Science ®Google Scholar
• Gurung B, Singh K, Paul RK, et al. An alternative method for forecasting price volatility by combining models. Commun Stat Simul Comput. 2017;46(6):4627–4636. doi:10.1080/03610918.2015.1124115
   Web of Science ®Google Scholar
• Xu Y, Xia Z, Wang C, et al. An empirical analysis of the price volatility characteristics of China's soybean futures market based on arima-gjr-garch model. J Math. 2021;2021:7765325.
   Web of Science ®Google Scholar
• Elfahham Y. Estimation and prediction of construction cost index using neural networks, time series, and regression. Alex Eng J. 2019;58(2):499–506. doi:10.1016/j.aej.2019.05.002
   Web of Science ®Google Scholar
• Roache MSK. What explains the rise in food price volatility? International Monetary Fund; 2010.
   Google Scholar
• Xu X, Thurman WN. Using local information to improve short-run corn cash price forecasts. 2015. doi:10.22004/ag.econ.285845
   Google Scholar
• Andreyeva T, Long MW, Brownell KD. The impact of food prices on consumption: a systematic review of research on the price elasticity of demand for food. Am J Public Health. 2010;100(2):216–222. doi:10.2105/AJPH.2008.151415
   PubMed Web of Science ®Google Scholar
• Xu X. Price discovery in us corn cash and futures markets: the role of cash market selection. 2014. doi:10.22004/ag.econ.169809
   Google Scholar
• Xu Z, Deng H, Wu Q. Prediction of soybean price trend via a synthesis method with multistage model. Int J Agric Environ Inform Syst (IJAEIS). 2021;12(4):1–13. doi:10.4018/IJAEIS.20211001.oa1
   Web of Science ®Google Scholar
• Schroeter C, Lusk J, Tyner W. Determining the impact of food price and income changes on body weight. J Health Econ. 2008;27(1):45–68. doi:10.1016/j.jhealeco.2007.04.001
   PubMed Web of Science ®Google Scholar
• Abbott PC, Hurt C, Tyner WE. What's driving food prices? March 2009 update. Technical Report. 2009. doi:10.22004/ag.econ.48495
   Google Scholar
• Xu X. Contemporaneous and granger causality among us corn cash and futures prices. Eur Rev Agric Econ. 2019;46(4):663–695. doi:10.1093/erae/jby036
   Web of Science ®Google Scholar
• Abbott P, Borot de Battisti A. Recent global food price shocks: causes, consequences and lessons for african governments and donors. J Afr Econ. 2011;20(Supplement 1):i12–i62. doi:10.1093/jae/ejr007
   Web of Science ®Google Scholar
• Xu X. Price dynamics in corn cash and futures markets: cointegration, causality, and forecasting through a rolling window approach. Financial Mark Portf Manag. 2019;33(2):155–181. doi:10.1007/s11408-019-00330-7
   Web of Science ®Google Scholar
• Kling JL, Bessler DA. A comparison of multivariate forecasting procedures for economic time series. Int J Forecast. 1985;1(1):5–24. doi:10.1016/S0169-2070(85)80067-4
   Google Scholar
• Bessler DA. Adaptive expectations the exponentially weighted forecast, and optimal statistical predictors: A revisit. Agric Econ Res. 1982;34:16–23. doi:10.22004/ag.econ.148819
   Google Scholar
• Xu X, Thurman W. Forecasting local grain prices: an evaluation of composite models in 500 corn cash markets, 2015. doi:10.22004/ag.econ.205332
   Google Scholar
• Brandt JA, Bessler DA. Composite forecasting: an application with us hog prices. Am J Agric Econ. 1981;63(1):135–140. doi:10.2307/1239819
   Web of Science ®Google Scholar
• Bessler DA, Chamberlain PJ. Composite forecasting with dirichlet priors. Decis Sci. 1988;19(4):771–781. doi:10.1111/j.1540-5915.1988.tb00302.x
   Web of Science ®Google Scholar
• Xu X, Zhang Y. House price information flows among some major Chinese cities: linear and nonlinear causality in time and frequency domains. Int J Hous Mark Anal. 2022. doi:10.1108/IJHMA-07-2022-0098
   Web of Science ®Google Scholar
• McIntosh CS, Bessler DA. Forecasting agricultural prices using a bayesian composite approach. J Agric Appl Econ. 1988;20(2):73–80. doi:10.1017/S0081305200017611
   Google Scholar
• Bessler DA, Brandt JA. Forecasting livestock prices with individual and composite methods. Appl Econ. 1981;13(4):513–522. doi:10.1080/00036848100000016
   Web of Science ®Google Scholar
• Xu X, Zhang Y. Contemporaneous causality among regional steel price indices of east, south, north, central south, northeast, southwest, and northwest China. Miner Econ. 2023;1:1. doi:10.1007/s13563-023-00380-4
   Web of Science ®Google Scholar
• Bessler DA. Forecasting multiple time series with little prior information. Am J Agric Econ. 1990;72(3):788–792. doi:10.2307/1243059
   Web of Science ®Google Scholar
• Bessler DA, Babula RA. Forecasting wheat exports: do exchange rates matter? J Bus Econ Stat. 1987;5:397–406. doi:10.2307/1391615
   Web of Science ®Google Scholar
• Xu X, Zhang Y. Dynamic relationships among composite property prices of major Chinese cities: contemporaneous causality through vector error corrections and directed acyclic graphs. Int J Real Estate Stud. 2023.
   Google Scholar
• Brandt JA, Bessler DA. Forecasting with a dynamic regression model: a heuristic approach. North Cent J Agric Econ. 1982;4:27–33. doi:10.2307/1349096
   Google Scholar
• Brandt JA, Bessler DA. Forecasting with vector autoregressions versus a univariate arima process: an empirical example with us hog prices. North Cent J Agric Econ. 1984;4:29–36. doi:10.2307/1349248
   Google Scholar
• Xu X. Causality, price discovery, and price forecasts: evidence from us corn cash and futures markets. 2015.
   Google Scholar
• Brandt JA, Bessler DA. Price forecasting and evaluation: an application in agriculture. J Forecast. 1983;2(3):237–248. doi:10.1002/for.3980020306
   Web of Science ®Google Scholar
• Yang J, Haigh MS, Leatham DJ. Agricultural liberalization policy and commodity price volatility: a garch application. Appl Econ Lett. 2001;8(9):593–598. doi:10.1080/13504850010018734
   Web of Science ®Google Scholar
• Bessler DA, Yang J, Wongcharupan M. Price dynamics in the international wheat market: modeling with error correction and directed acyclic graphs. J Reg Sci. 2003;43(1):1–33. doi:10.1111/1467-9787.00287
   Web of Science ®Google Scholar
• Bessler DA, Brandt JA. An analysis of forecasts of livestock prices. J Econ Behav Organ. 1992;18(2):249–263. doi:10.1016/0167-2681(92)90030-F
   Web of Science ®Google Scholar
• Bessler DA, Hopkins JC. Forecasting an agricultural system with random walk priors. Agric Syst. 1986;21(1):59–67. doi:10.1016/0308-521X(86)90029-6
   Web of Science ®Google Scholar
• Chen DT, Bessler DA. Forecasting monthly cotton price: structural and time series approaches. Int J Forecast. 1990;6(1):103–113. doi:10.1016/0169-2070(90)90101-G
   Web of Science ®Google Scholar
• Wang Z, Bessler DA. Forecasting performance of multivariate time series models with full and reduced rank: an empirical examination. Int J Forecast. 2004;20(4):683–695. doi:10.1016/j.ijforecast.2004.01.002
   Web of Science ®Google Scholar
• Chen DT, Bessler DA. 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: 1987. doi:10.22004/ag.econ.285463
   Google Scholar
• Bessler DA, Kling JL. Forecasting vector autoregressions with bayesian priors. Am J Agric Econ. 1986;68(1):144–151. doi:10.2307/1241659
   Web of Science ®Google Scholar
• Babula RA, Bessler DA, Reeder J, et al. Modeling us soy-based markets with directed acyclic graphs and bernanke structural var methods: the impacts of high soy meal and soybean prices. J Food Distrib Res. 2004;35:29–52. doi:10.22004/ag.econ.27559
   Google Scholar
• Xu X. Cointegration and price discovery in us corn markets. In: Agricultural and resource economics seminar series, North Carolina State University; 2014. doi:10.13140/RG.2.2.30153.49768
   Google Scholar
• Yang J, Zhang J, Leatham DJ. Price and volatility transmission in international wheat futures markets. Ann Econ Financ. 2003;4:37–50. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.295.2182/&rep=rep1/&type=pdf.
   Google Scholar
• Awokuse TO, Yang J. The informational role of commodity prices in formulating monetary policy: a reexamination. Econ Lett. 2003;79(2):219–224. doi:10.1016/S0165-1765(02)00331-2
   Web of Science ®Google Scholar
• Yang J, Awokuse TO. Asset storability and hedging effectiveness in commodity futures markets. Appl Econ Lett. 2003;10(8):487–491. doi:10.1080/1350485032000095366
   Web of Science ®Google Scholar
• Yang J, Leatham DJ. Market efficiency of us grain markets: application of cointegration tests. Agribus: An Int J. 1998;14:107–112.
   Google Scholar
• Yang J, Li Z, Wang T. Price discovery in Chinese agricultural futures markets: a comprehensive look. J Futures Mark. 2021;41(4):536–555. doi:10.1002/fut.22179
   Web of Science ®Google Scholar
• Wang J, Dharmasena S, Bessler DA. Price dynamics and forecasts of world and China vegetable oil markets. 2013. doi:10.22004/ag.econ.151150
   Google Scholar
• Xu X, Zhang Y. Cointegration between housing prices: evidence from one hundred Chinese cities. J Prop Res. 2023;40(1):53–75. doi:10.1080/09599916.2022.2114926
   Web of Science ®Google Scholar
• Ma Y. Do iron ore scrap steel, carbon emission allowance, and seaborne transportation prices drive steel price fluctuations? Resour Policy. 2021;72:102115. doi:10.1016/j.resourpol.2021.102115
   Web of Science ®Google Scholar
• Ma Y, Wang J. Time-varying spillovers and dependencies between iron ore scrap steel, carbon emission, seaborne transportation, and China's steel stock prices. Resour Policy. 2021;74:102254. doi:10.1016/j.resourpol.2021.102254
   Web of Science ®Google Scholar
• Hardin K. Factors affecting forecast accuracy of scrap price in the US Steel Industry [dissertation]. Walden University; 2011.
   Google Scholar
• Albertson K, Aylen J. Forecasting using a periodic transfer function: with an application to the uk price of ferrous scrap. Int J Forecast. 1999;15(4):409–419. doi:10.1016/S0169-2070(99)00020-5
   Web of Science ®Google Scholar
• Adli KA, Sener U. Forecasting of the us steel prices with lvar and vec models. Bus Econ Res J. 2021;12(3):509–522. doi:10.20409/berj.2021.335
   Google Scholar
• Malanichev A. System for scenario planning and forecasting world prices for steel and metallurgical raw materials. Stud Russ Econ Dev. 2014;25(3):251–258. doi:10.1134/S1075700714030071
   Google Scholar
• Albertson K, Aylen J. Modelling the great lakes freeze: forecasting and seasonality in the market for ferrous scrap. Int J Forecast. 1996;12(3):345–359. doi:10.1016/0169-2070(96)00669-3
   Web of Science ®Google Scholar
• Xiarchos IM, Fletcher JJ. Price and volatility transmission between primary and scrap metal markets. Resour Conserv Recycl. 2009;53(12):664–673. doi:10.1016/j.resconrec.2009.04.020
   Web of Science ®Google Scholar
• Zhang Y, Xu X. Gaussian process modeling of doped-lanthanum manganites curie temperature from compositions and ionic radii. Solid State Commun. 2023;360:115025. doi:10.1016/j.ssc.2022.115025
   Web of Science ®Google Scholar
• Xu X, Zhang Y. High-frequency csi300 futures trading volume predicting through the neural network. Asian J Econ Bank. 2023. doi:10.1108/AJEB-05-2022-0051
   Google Scholar
• dos Reis Filho IJ, Correa GB, Freire GM, et al. Forecasting future corn and soybean prices: an analysis of the use of textual information to enrich time-series. In: Anais do VIII Symposium on Knowledge Discovery, Mining and Learning, SBC, 2020. p. 113–120. doi:10.5753/kdmile.2020.11966
   Google Scholar
• Li J, Li G, Liu M, et al. A novel text-based framework for forecasting agricultural futures using massive online news headlines. Int J Forecast. 2020;38(1):35–50. doi:10.1016/j.ijforecast.2020.02.002.
   Web of Science ®Google Scholar
• Yoosefzadeh-Najafabadi M, Earl HJ, Tulpan D, et al. Application of machine learning algorithms in plant breeding: predicting yield from hyperspectral reflectance in soybean. Front Plant Sci. 2021;11:2169. doi:10.3389/fpls.2020.624273
   Web of Science ®Google Scholar
• Ribeiro MHD, dos Santos Coelho L. Ensemble approach based on bagging boosting and stacking for short-term prediction in agribusiness time series. Appl Soft Comput. 2020;86:105837. doi:10.1016/j.asoc.2019.105837
   Web of Science ®Google Scholar
• Zhao H. Futures price prediction of agricultural products based on machine learning. Neural Comput Appl. 2021;33(3):837–850. doi:10.1007/s00521-020-05250-6
   Web of Science ®Google Scholar
• Jiang F, He J, Zeng Z. Pigeon-inspired optimization and extreme learning machine via wavelet packet analysis for predicting bulk commodity futures prices. Sci China Inf Sci. 2019;62:1–19. doi:10.1007/s11432-018-9714-5
   PubMed Web of Science ®Google Scholar
• Handoyo S, Chen YP. The developing of fuzzy system for multiple time series forecasting with generated rule bases and optimized consequence part. SSRG Int J Eng Trends Technol. 2020;68(12):118–122. doi:10.14445/22315381/IJETT-V68I12P220
   Google Scholar
• Silalahi DD. Application of neural network model with genetic algorithm to predict the international price of crude palm oil (cpo) and soybean oil (sbo), In: 12th National Convention on Statistics (NCS), Philippine: Mandaluyong City; 2013. p. 1–2.
   Google Scholar
• Xu X, Zhang Y. Canola and soybean oil price forecasts via neural networks. Adv Comput Intell. 2022;2(5):32. doi:10.1007/s43674-022-00045-9
   Google Scholar
• Li G, Chen W, Li D, et al. Comparative study of short-term forecasting methods for soybean oil futures based on lstm, svr, es and wavelet transformation, In: Journal of Physics: Conference Series, Vol. 1682, IOP Publishing; 2020. p. 012007. doi:10.1088/1742-6596/1682/1/012007
   Google Scholar
• Kanchymalay K, Salim N, Sukprasert A, et al. Multivariate time series forecasting of crude palm oil price using machine learning techniques. In: IOP Conference Series: Materials Science and Engineering, Vol. 226, IOP Publishing; 2017. p. 012117. doi:10.1088/1757-899X/226/1/012117
   Google Scholar
• Surjandari I, Naffisah MS, Prawiradinata MI. Text mining of twitter data for public sentiment analysis of staple foods price changes. J Ind Intell Inf. 2015;3:253–257. doi:10.12720/jiii.3.3.253-257.
   Google Scholar
• Ribeiro CO, Oliveira SM. A hybrid commodity price-forecasting model applied to the sugar–alcohol sector. Aust J Agric Resour Econ. 2011;55(2):180–198.
   Web of Science ®Google Scholar
• Zhang J, Meng Y, Wei J, et al. A novel hybrid deep learning model for sugar price forecasting based on time series decomposition. Math Prob Eng. 2021;2021:1. doi:10.1155/2021/6507688
   Web of Science ®Google Scholar
• Melo Bd., Milioni AZ, Nascimento Júnior CL. Daily and monthly sugar price forecasting using the mixture of local expert models. Pesq Oper. 2007;27(2):235–246. doi:10.1590/S0101-74382007000200003
   Google Scholar
• de Melo B, Júnior CN, Milioni AZ. Daily sugar price forecasting using the mixture of local expert models. WIT Trans Inf Commun Technol. 2004;33:231–240. doi:10.2495/DATA040221.
   Google Scholar
• Silva N, Siqueira I, Okida S, et al. Neural networks for predicting prices of sugarcane derivatives. Sugar Tech. 2019;21(3):514–523. doi:10.1007/s12355-018-0648-5
   Web of Science ®Google Scholar
• Xu X, Zhang Y. Corn cash price forecasting with neural networks. Comput Electron Agric. 2021;184:106120. doi:10.1016/j.compag.2021.106120
   Web of Science ®Google Scholar
• Mayabi TW. An artificial neural network model for predicting retail maize prices in Kenya [dissertation] University of Nairobi; 2019.
   Google Scholar
• Xu X, Zhang Y. Corn cash-futures basis forecasting via neural networks. Adv Comput Intell. 2023;3(2):8. doi:10.1007/s43674-023-00054-2
   Google Scholar
• Moreno RS, Salazar OZ. An artificial neural network model to analyze maize price behavior in mexico. Appl Math (Irvine). 2018;09(05):473–487. doi:10.4236/am.2018.95034
   Google Scholar
• Xu X, Zhang Y. Yellow corn wholesale price forecasts via the neural network. EconomiA. 2023. doi:10.1108/ECON-05-2022-0026
   Google Scholar
• Zelingher R, Makowski D, Brunelle T. Assessing the sensitivity of global maize price to regional productions using statistical and machine learning methods. Front Sustain Food Syst. 2021;5:171. doi:10.3389/fsufs.2021.655206
   Google Scholar
• Shahhosseini M, Hu G, Huber I, et al. Coupling machine learning and crop modeling improves crop yield prediction in the us corn belt. Sci Rep. 2021;11(1):1–15. doi:10.1038/s41598-020-80820-1
   PubMed Web of Science ®Google Scholar
• Shahhosseini M, Hu G, Archontoulis S. Forecasting corn yield with machine learning ensembles. Front Plant Sci. 2020;11:1120. doi:10.3389/fpls.2020.01120
   PubMed Web of Science ®Google Scholar
• Zelingher R, Makowski D, Brunelle T. Forecasting impacts of agricultural production on global maize price. 2020.
   Google Scholar
• M. Ribeiro MHD, Ribeiro VHA, Reynoso-Meza G, et al. Multi-objective ensemble model for short-term price forecasting in corn price time series. In: 2019 International Joint Conference on Neural Networks (IJCNN). IEEE; 2019. p. 1–8. doi:10.1109/IJCNN.2019.8851880
   Google Scholar
• Ayankoya K, Calitz AP, Greyling JH. Using neural networks for predicting futures contract prices of white maize in south africa, In: Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists. 2016. p. 1–10. doi:10.1145/2987491.2987508
   Google Scholar
• Wan H, Zhou Y. Neural network model comparison and analysis of prediction methods using arima and lstm models. In: 2021 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), IEEE; 2021. p. 640–643. doi:10.1109/AEECA52519.2021.9574427
   Google Scholar
• Antwi E, Gyamfi EN, Kyei KA, et al. Modeling and forecasting commodity futures prices: decomposition approach. IEEE Access. 2022;10:27484–27503. doi:10.1109/ACCESS.2022.3152694
   Web of Science ®Google Scholar
• Fang Y, Guan B, Wu S, et al. Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices. J Forecast. 2020;39(6):877–886. doi:10.1002/for.2665
   Web of Science ®Google Scholar
• Kohzadi N, Boyd MS, Kermanshahi B, et al. A comparison of artificial neural network and time series models for forecasting commodity prices. Neurocomputing. 1996;10(2):169–181. doi:10.1016/0925-2312(95)00020-8
   Web of Science ®Google Scholar
• Zou H, Xia G, Yang F, et al. An investigation and comparison of artificial neural network and time series models for Chinese food grain price forecasting. Neurocomputing. 2007;70(16-18):2913–2923. doi:10.1016/j.neucom.2007.01.009
   Web of Science ®Google Scholar
• Rasheed A, Younis MS, Ahmad F, et al. District wise price forecasting of wheat in pakistan using deep learning. arXiv preprint arXiv:2103.04781. 2021.
   Google Scholar
• Khamis A, Abdullah S. Forecasting wheat price using backpropagation and narx neural network. Int J Eng Sci. 2014;3:19–26.
   Google Scholar
• Dias J, Rocha H. Forecasting wheat prices based on past behavior: comparison of different modelling approaches, In: International Conference on Computational Science and Its Applications. Springer; 2019. p. 167–182. doi:10.1007/978-3-030-24302-9_13
   Google Scholar
• Gómez D, Salvador P, Sanz J, et al. Modelling wheat yield with antecedent information, satellite and climate data using machine learning methods in mexico. Agric Forest Meteorol. 2021;300:108317. doi:10.1016/j.agrformet.2020.108317
   Web of Science ®Google Scholar
• Bakhtadze N, Maximov E, Maximova N. Local wheat price prediction models. In: 2021 IEEE 7th International Conference on Control Science and Systems Engineering (ICCSSE), IEEE; 2021. p. 223–227. doi:10.1109/ICCSSE52761.2021.9545154
   Google Scholar
• Kouadio L, Deo RC, Byrareddy V, et al. Artificial intelligence approach for the prediction of robusta coffee yield using soil fertility properties. Comput Electron Agric. 2018;155:324–338. doi:10.1016/j.compag.2018.10.014
   Web of Science ®Google Scholar
• Abreham Y. Coffee price prediction using machine-learning techniques [dissertation] ASTU, 2019.
   Google Scholar
• HUY HT, THAC HN, THU HNT, et al. Econometric combined with neural network for coffee price forecasting. J Appl Econ Sci. 2019;14:378–392.
   Google Scholar
• Degife WA, Sinamo A. Efficient predictive model for determining critical factors affecting commodity price: the case of coffee in ethiopian commodity exchange (ecx). Int J Inf Eng Electron Bus. 2019;11:32–36. doi:10.5815/ijieeb.2019.06.05
   Google Scholar
• Naveena K, Subedar S. Hybrid time series modelling for forecasting the price of washed coffee (arabica plantation coffee) in india, International Journal of Agriculture Sciences, ISSN (2017) p. 0975–3710.
   Google Scholar
• Lopes LP. Prediction of the brazilian natural coffee price through statistical machine learning models. SIGMAE. 2018;7:1–16.
   Web of Science ®Google Scholar
• Deina C, do Amaral Prates MH, Alves CHR, et al. A methodology for coffee price forecasting based on extreme learning machines. Inf Process Agric. 2021;1:1. doi:10.1016/j.inpa.2021.07.003
   Google Scholar
• Harris JJ. A machine learning approach to forecasting consumer food prices. 2017.
   Google Scholar
• Ali M, Deo RC, Downs NJ, et al. Cotton yield prediction with Markov chain monte Carlo-based simulation model integrated with genetic programing algorithm: a new hybrid copula-driven approach. Agric For Meteorol. 2018;263:428–448. doi:10.1016/j.agrformet.2018.09.002
   Web of Science ®Google Scholar
• Shahwan T, Odening M. Forecasting agricultural commodity prices using hybrid neural networks, In: Computational intelligence in economics and finance. Springer; 2007. p. 63–74. doi:10.1007/978-3-540-72821-4_3
   Google Scholar
• Filippi P, Jones EJ, Wimalathunge NS, et al. An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning. Precis Agric. 2019;20(5):1015–1029. doi:10.1007/s11119-018-09628-4
   Web of Science ®Google Scholar
• Wen G, Ma B-L, Vanasse A, et al. Machine learning-based canola yield prediction for site-specific nitrogen recommendations. Nutr Cycling Agroecosyst. 2021;121(2-3):241–256. doi:10.1007/s10705-021-10170-5
   Web of Science ®Google Scholar
• Singh A, Mishra G. Application of box-jenkins method and artificial neural network procedure for time series forecasting of prices. Stat Transit New Series. 2015;16(1):83–96.
   Google Scholar
• Mishra G, Singh A. A study on forecasting prices of groundnut oil in delhi by arima methodology and artificial neural networks. Agris on-line Pap Econ Inf. 2013;5:25–34. doi:10.22004/ag.econ.157527
   Google Scholar
• Zong J, Zhu Q. Price forecasting for agricultural products based on bp and rbf neural network. In: 2012 IEEE International Conference on Computer Science and Automation Engineering, IEEE; 2012. p. 607–610. doi:10.1109/ICSESS.2012.6269540
   Google Scholar
• Yin Y, Zhu Q. Effect of magnitude differences in the raw data on price forecasting using rbf neural network, In: 2012 11th International Symposium on Distributed Computing and Applications to Business, Engineering & Science. IEEE; 2012. p. 237–240. doi:10.1109/DCABES.2012.19
   Google Scholar
• Zong J, Zhu Q. Apply grey prediction in the agriculture production price. In: 2012 Fourth International Conference on Multimedia Information Networking and Security. IEEE; 2012. p. 396–399. doi:10.1109/MINES.2012.78
   Google Scholar
• Quan-Yin Z, Yong-Hu Y, Yun-Yang Y, et al. A novel efficient adaptive sliding window model for week-ahead price forecasting. TELKOMNIKA Indones J Electr Eng. 2014;12:2219–2226. doi:10.11591/telkomnika.v12i3.4490
   Google Scholar
• Zhu Q-Y, Yin Y-H, Zhu H-J, et al. Effect of magnitude differences in the original data on price forecasting. J Algorithm Comput Technol. 2014;8(4):389–420. doi:10.1260/1748-3018.8.4.389
   Google Scholar
• Xiong T, Li C, Bao Y. Seasonal forecasting of agricultural commodity price using a hybrid stl and elm method: evidence from the vegetable market in China. Neurocomputing. 2018;275:2831–2844. doi:10.1016/j.neucom.2017.11.053
   Web of Science ®Google Scholar
• Li Y, Li C, Zheng M. A hybrid neural network and h-p filter model for short-term vegetable price forecasting. Math Prob Eng. 2014;2014:135862. doi:10.1155/2014/135862.
   Web of Science ®Google Scholar
• Myat AK, Tun MTZ. Predicting palm oil price direction using random forest. In: 2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE). IEEE; 2019. p. 1–6. doi:10.1109/ICTKE47035.2019.8966799
   Google Scholar
• Singh A. Comparison of artificial neural networks and statistical methods for forecasting prices of different edible oils in indian markets. Int Res J Modern Eng Technol Sci. 2021;3:1044–1050.
   Google Scholar
• Lama A, Jha GK, Gurung B, et al. A comparative study on time-delay neural network and garch models for forecasting agricultural commodity price volatility. 2016.
   Google Scholar
• Jaiswal R, Jha GK, Kumar RR, et al. Deep long short-term memory based model for agricultural price forecasting. Neural Comput Appl. 2022;34(6):4661–4676. doi:10.1007/s00521-021-06621-3
   Web of Science ®Google Scholar
• Gollou AR, Ghadimi N. A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets. J Intell Fuzzy Syst. 2017;32(6):4031–4045. doi:10.3233/JIFS-152073
   Web of Science ®Google Scholar
• Saâdaoui F. A seasonal feedforward neural network to forecast electricity prices. Neural Comput Appl. 2017;28(4):835–847. doi:10.1007/s00521-016-2356-y
   Web of Science ®Google Scholar
• Zou Y, Tu M, Teng X, et al. Electricity price forecast based on stacked autoencoder in spot market environment. In: 2019 9th International Conference on Power and Energy Systems (ICPES). IEEE; 2019. p. 1–6. doi:10.1109/ICPES47639.2019.9105616
   Google Scholar
• Abhinav R, Pindoriya NM. Electricity price forecast for optimal energy management for wind power producers: a case study in indian power market, in: 2018 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia). IEEE; 2018. p. 1233–1238. doi:10.1109/ISGT-Asia.2018.8467870
   Google Scholar
• Abedinia O, Amjady N, Shafie-Khah M, et al. Electricity price forecast using combinatorial neural network trained by a new stochastic search method. Energy Conv Manag. 2015;105:642–654. doi:10.1016/j.enconman.2015.08.025
   Web of Science ®Google Scholar
• Sun G, Chen T, Wei Z, et al. A carbon price forecasting model based on variational mode decomposition and spiking neural networks. Energies. 2016;9(1):54. doi:10.3390/en9010054
   Web of Science ®Google Scholar
• Zhou J, Wang S. A carbon price prediction model based on the secondary decomposition algorithm and influencing factors. Energies. 2021;14(5):1328. doi:10.3390/en14051328
   Web of Science ®Google Scholar
• Hao Y, Tian C. A hybrid framework for carbon trading price forecasting: the role of multiple influence factor. J Clean Prod. 2020;262:120378. doi:10.1016/j.jclepro.2020.120378
   Web of Science ®Google Scholar
• Huang Y, Dai X, Wang Q, et al. A hybrid model for carbon price forecasting using garch and long short-term memory network. Appl Energy. 2021;285:116485. doi:10.1016/j.apenergy.2021.116485
   Web of Science ®Google Scholar
• Zhang J, Li D, Hao Y, et al. A hybrid model using signal processing technology econometric models and neural network for carbon spot price forecasting. J Clean Prod. 2018;204:958–964. doi:10.1016/j.jclepro.2018.09.071
   Web of Science ®Google Scholar
• Sohrabi P, Jodeiri Shokri B, Dehghani H. Predicting coal price using time series methods and combination of radial basis function (rbf) neural network with time series. Min Econ. 2021;36:1–10. doi:10.1007/s13563-021-00286-z.
   Web of Science ®Google Scholar
• Jaipuria S. Prediction of lam coke price using ann and anfis model. Int J Appl Res Manag Econ. 2019;2(3):7–17. doi:10.33422/ijarme.v2i3.267
   Google Scholar
• Matyjaszek M, Fernández PR, Krzemień A, et al. Forecasting coking coal prices by means of arima models and neural networks considering the transgenic time series theory. Resour Policy. 2019;61:283–292. doi:10.1016/j.resourpol.2019.02.017
   Web of Science ®Google Scholar
• Zhang K, Cao H, Thé J, et al. A hybrid model for multi-step coal price forecasting using decomposition technique and deep learning algorithms. Appl Energy. 2022;306:118011. doi:10.1016/j.apenergy.2021.118011
   Web of Science ®Google Scholar
• Alameer Z, Fathalla A, Li K, et al. Multistep-ahead forecasting of coal prices using a hybrid deep learning model. Resour Policy. 2020;65:101588. doi:10.1016/j.resourpol.2020.101588
   Web of Science ®Google Scholar
• Zhang Y, Hamori S. Forecasting crude oil market crashes using machine learning technologies. Energies. 2020;13(10):2440. doi:10.3390/en13102440
   Web of Science ®Google Scholar
• Paul C, Nwosu I, Ezenwanyi G, et al. The optimal machine learning modeling of brent crude oil price. Quart J Econ Res. 2021;7:31–43. doi:10.18488/journal.88.2021.71.31.43
   Google Scholar
• Abdollahi H, Ebrahimi SB. A new hybrid model for forecasting brent crude oil price. Energy. 2020;200:117520. doi:10.1016/j.energy.2020.117520
   Web of Science ®Google Scholar
• Jabeur SB, Khalfaoui R, Arfi WB. The effect of green energy,global environmental indexes, and stock markets in predicting oil price crashes: evidence from explainable machine learning. J Environ Manag. 2021;298:113511. doi:10.1016/j.jenvman.2021.113511
   PubMed Web of Science ®Google Scholar
• Lu Q, Sun S, Duan H, et al. Analysis and forecasting of crude oil price based on the variable selection-lstm integrated model. Energy Inform. 2021;4(S2):1–20. doi:10.1186/s42162-021-00166-4
   Google Scholar
• Mustaffa Z, Yusof Y, Kamaruddin SS. An enhanced artificial bee colony optimizer for predictive analysis of heating oil prices using least squares support vector machines, in: Biologically-inspired techniques for knowledge discovery and data mining, IGI Global; 2014. p. 149–173. doi:10.4018/978-1-4666-6078-6.ch007
   Google Scholar
• Tian L, Chen H, Zhen Z. Research on the forward-looking behavior judgment of heating oil price evolution based on complex networks. PLoS ONE. 2018;13(9):e0202209. doi:10.1371/journal.pone.0202209
   PubMed Web of Science ®Google Scholar
• Wang B, Wang J. Energy futures prices forecasting by novel dpfwr neural network and ds-cid evaluation. Neurocomputing. 2019;338:1–15. doi:10.1016/j.neucom.2019.01.092
   Web of Science ®Google Scholar
• Chiroma H, Abdul-Kareem S, Muaz SA, et al. Neural network intelligent learning algorithm for inter-related energy products applications, In: International Conference in Swarm Intelligence. Springer; 2014. p. 284–293. doi:10.1007/978-3-319-11857-4_32
   Google Scholar
• Malliaris ME, Malliaris SG. Forecasting energy product prices, In: Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., Vol. 5, IEEE; 2005. p. 3284–3289. doi:10.1109/IJCNN.2005.1556454
   Google Scholar
• Mouchtaris D, Sofianos E, Gogas P, et al. Forecasting natural gas spot prices with machine learning. Energies. 2021;14(18):5782. doi:10.3390/en14185782
   Web of Science ®Google Scholar
• Wang J, Cao J, Yuan S, et al. Short-term forecasting of natural gas prices by using a novel hybrid method based on a combination of the ceemdan-se-and the pso-als-optimized gru network. Energy. 2021;233:121082. doi:10.1016/j.energy.2021.121082
   Web of Science ®Google Scholar
• Li J, Wu Q, Tian Y, et al. Monthly henry hub natural gas spot prices forecasting using variational mode decomposition and deep belief network. Energy. 2021;227:120478. doi:10.1016/j.energy.2021.120478
   Web of Science ®Google Scholar
• Sahed A, Mekidiche M, Kahoui H. Forecasting natural gas prices using nonlinear autoregressive neural network. Int J Math Sci Comput. 2020;5:37–46. doi:10.5815/ijmsc.2020.05.04
   Google Scholar
• Wang C, Xu J, Xu K, et al. Rolling forecast nature gas spot price with back propagation neural network, In: 2019 IEEE Sustainable Power and Energy Conference (iSPEC). IEEE; 2019. p. 2473–2477. doi:10.1109/iSPEC48194.2019.8974910
   Google Scholar
• Gligorić Z, Gligorić M, Halilović D, et al. Hybrid stochastic-grey model to forecast the behavior of metal price in the mining industry. Sustainability. 2020;12(16):6533. doi:10.3390/su12166533
   Web of Science ®Google Scholar
• Alameer Z, Abd Elaziz M, Ewees AA, et al. Forecasting copper prices using hybrid adaptive neuro-fuzzy inference system and genetic algorithms. Natural Resour Res. 2019;28(4):1385–1401. doi:10.1007/s11053-019-09473-w
   Web of Science ®Google Scholar
• Huynh TLD. The effect of uncertainty on the precious metals market: new insights from transfer entropy and neural network var. Resour Policy. 2020;66:101623. doi:10.1016/j.resourpol.2020.101623
   Web of Science ®Google Scholar
• Pierdzioch C, Risse M. Forecasting precious metal returns with multivariate random forests. Empir Econ. 2020;58(3):1167–1184. doi:10.1007/s00181-018-1558-9
   Web of Science ®Google Scholar
• Faghih Mohammadi Jalali M, Heidari H. Forecasting palladium price using gm (1, 1). Global Anal Discrete Math. 2018;3:1–9. doi:10.22128/GADM.2018.114
   Google Scholar
• Çelik U, BaÇarır Ç.. The prediction of precious metal prices via artificial neural network by using rapidminer. Alpha J. 2017;5(1):45–45. doi:10.17093/alphanumeric.290381
   Google Scholar
• Pierdzioch C, Risse M, Rohloff S. Are precious metals a hedge against exchange-rate movements? an empirical exploration using bayesian additive regression trees. North Am J Econom Finance. 2016;38:27–38. doi:10.1016/j.najef.2016.06.002
   Web of Science ®Google Scholar
• Erkan TE, Karaçor AG. On predictability of precious metals towards robust trading. Int Sci J ‘Industry 4.0’. 2020;5:87–89.
   Google Scholar
• Sadorsky P. Predicting gold and silver price direction using tree-based classifiers. J Risk Finan Manage. 2021;14(5):198. doi:10.3390/jrfm14050198
   Web of Science ®Google Scholar
• Aruna S, Umamaheswari P, Sujipriya J. Prediction of potential gold prices using machine learning approach. Ann Roman Soc Cell Biol. 2021;25:1385–1396.
   Google Scholar
• Jabeur SB, Mefteh-Wali S, Viviani J-L. Forecasting gold price with the xgboost algorithm and shap interaction values. Ann Oper Res. 2021;1–21. doi:10.1007/s10479-021-04187-w
   Web of Science ®Google Scholar
• Yuan F-C, Lee C-H, Chiu C. Using market sentiment analysis and genetic algorithm-based least squares support vector regression to predict gold prices. Int J Comput Intell Syst. 2020;13(1):234–246. doi:10.2991/ijcis.d.200214.002
   Web of Science ®Google Scholar
• Tang B-q., Han J, Guo G-f., et al. Building material prices forecasting based on least square support vector machine and improved particle swarm optimization. Archit Eng Desig Manag. 2019;15:196–212. doi:10.1080/17452007.2018.1556577
   Web of Science ®Google Scholar
• Xu X, Zhang Y. Regional steel price index forecasts with neural networks: evidence from east,south, north, central south, northeast, southwest, and northwest China. J Supercomput. 2023. doi:10.1007/s11227-023-05207-1
   PubMed Web of Science ®Google Scholar
• Shyu Y-W, Chang C-C. A hybrid model of memd and pso-lssvr for steel price forecasting. Int J Eng Manag Res. 2022;12:130–40. doi:10.31033/ijemr.12.1.5
   Google Scholar
• Mir M, Kabir HD, Nasirzadeh F, et al. Neural network-based interval forecasting of construction material prices. J Build Eng. 2021;39:102288. doi:10.1016/j.jobe.2021.102288
   Web of Science ®Google Scholar
• Ganokratanaa T, Ketcham M. Deep index price forecasting in steel industry. In: 2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE; 2021. p. 1–6. doi:10.1109/JCSSE53117.2021.9493843
   Google Scholar
• Liu Z, Wang Y, Zhu S, et al. Steel prices index prediction in China based on bp neural network. In: LISS 2014, Springer; 2015. p. 603–608. doi:10.1007/978-3-662-43871-8_87
   Google Scholar
• Benrhmach G, Namir K, Namir A, et al. Nonlinear autoregressive neural network and extended kalman filters for prediction of financial time series. J Appl Math. 2020;2020:1–6.doi:10.1155/2020/5057801
   Web of Science ®Google Scholar
• Wu B, Zhu Q. Week-ahead price forecasting for steel market based on rbf nn and asw. In: 2012 IEEE International Conference on Computer Science and Automation Engineering. IEEE; 2012. p. 729–732. doi:10.1109/ICSESS.2012.6269570
   Google Scholar
• Eroglu Y, Sakar IK. Price modeling for steel industry: a case for Turkey.
   Google Scholar
• Yuan CZ, San WW, Leong TW. Determining optimal lag time selection function with novel machine learning strategies for better agricultural commodity prices forecasting in malaysia, in: Proceedings of the 2020 2nd International Conference on Information Technology and Computer Communications. 2020. p. 37–42. doi:10.1145/3417473.3417480
   Google Scholar
• Zhang D, Chen S, Liwen L, et al. Forecasting agricultural commodity prices using model selection framework with time series features and forecast horizons. IEEE Access. 2020;8:28197–28209. doi:10.1109/ACCESS.2020.2971591
   Web of Science ®Google Scholar
• Jha GK, Sinha K. Time-delay neural networks for time series prediction: an application to the monthly wholesale price of oilseeds in india. Neural Comput Appl. 2014;24(3-4):563–571. doi:10.1007/s00521-012-1264-z
   Web of Science ®Google Scholar
• RL M, Mishra AK. Forecasting spot prices of agricultural commodities in india: application of deep-learning models. Intell Syst Account Finance Manag. 2021;28(1):72–83. doi:10.1002/isaf.1487
   Web of Science ®Google Scholar
• Bayona-Oré S, Cerna R, Tirado Hinojoza E. Machine learning for price prediction for agricultural products. 2021. doi:10.37394/23207.2021.18.92
   Google Scholar
• Majid R. Advances in statistical forecasting methods: an overview. Econ Affairs. 2018;63(4):295479. doi:10.30954/0424-2513.4.2018.5
   Google Scholar
• Wang L, Feng J, Sui X, et al. Agricultural product price forecasting methods: research advances and trend. British Food J. 2020;122:2121–2138. doi:10.1108/BFJ-09-2019-0683.
   Web of Science ®Google Scholar
• Xu X, Zhang Y. Office property price index forecasting using neural networks. J Finan Manage Property Construct. 2023.
   Web of Science ®Google Scholar
• Karasu S, Altan A, Saraç Z, et al. Prediction of wind speed with non-linear autoregressive (nar) neural networks, In: 2017 25th Signal Processing and Communications Applications Conference (SIU). IEEE; 2017. p. 1–4. doi:10.1109/SIU.2017.7960507
   Google Scholar
• Xu X, Zhang Y. Commodity price forecasting via neural networks for coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat. Intell Syst Acc Financ Manag. 2022;29(3):169–181. doi:10.1002/isaf.1519
   Google Scholar
• Karasu S, Altan A, Saraç Z, et al. Estimation of fast varied wind speed based on narx neural network by using curve fitting. Int J Energy Appl Technol. 2017;4:137–146. https://dergipark.org.tr/en/download/article-file/354536.
   Google Scholar
• Xu X. Causal structure among us corn futures and regional cash prices in the time and frequency domain. J Appl Stat. 2018;45(13):2455–2480. doi:10.1080/02664763.2017.1423044
   Web of Science ®Google Scholar
• Yang J, Su X, Kolari JW. Do euro exchange rates follow a martingale? some out-of-sample evidence. J Bank Financ. 2008;32(5):729–740. doi:10.1016/j.jbankfin.2007.05.009
   Web of Science ®Google Scholar
• Xu X. Cointegration among regional corn cash prices. Econ Bull. 2015;35:2581–2594. http://www.accessecon.com/Pubs/EB/2015/Volume35/EB-15-V35-I4-P259.pdf
   Google Scholar
• Yang J, Cabrera J, Wang T. Nonlinearity data-snooping, and stock index etf return predictability. Eur J Oper Res. 2010;200(2):498–507. doi:10.1016/j.ejor.2009.01.009
   Web of Science ®Google Scholar
• Xu X. Cointegration and price discovery in us corn cash and futures markets. Empir Econ. 2018;55(4):1889–1923. doi:10.1007/s00181-017-1322-6
   Web of Science ®Google Scholar
• Wang T, Yang J. Nonlinearity and intraday efficiency tests on energy futures markets. Energy Econom. 2010;32:2496–503. doi:10.1016/j.eneco.2009.08.001
   Web of Science ®Google Scholar
• Xu X, Zhang Y. Individual time series and composite forecasting of the Chinese stock index. Mach Learning Appl. 2021;5:100035. doi:10.1016/j.mlwa.2021.100035
   Google Scholar
• Karasu S, Altan A, Bekiros S, et al. A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series. Energy. 2020;212:118750. doi:10.1016/j.energy.2020.118750
   Web of Science ®Google Scholar
• Xu X. Linear and nonlinear causality between corn cash and futures prices. J Agric Food Ind Organ. 2018;16(2):20160006. doi:10.1515/jafio-2016-0006
   Google Scholar
• Wegener C, von Spreckelsen C, Basse T, et al. Forecasting government bond yields with neural networks considering cointegration. J Forecast. 2016;35(1):86–92. doi:10.1002/for.2385
   Web of Science ®Google Scholar
• Xu X, Zhang Y. Network analysis of housing price comovements of a hundred Chinese cities. Natl Inst Econ Rev. 2022. doi:10.1017/nie.2021.34
   Web of Science ®Google Scholar
• Xu X, Zhang Y. Neural network predictions of the high-frequency csi300 first distant futures trading volume. Finan Markets Portfolio Manage. 2022. doi:10.1007/s11408-022-00421-y
   Web of Science ®Google Scholar
• Altan A, Karasu S, Zio E. A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer. Appl Soft Comput. 2021;100:106996. doi:10.1016/j.asoc.2020.106996
   Web of Science ®Google Scholar
• Xu X. Intraday price information flows between the csi300 and futures market: an application of wavelet analysis. Empir Econ. 2018;54(3):1267–1295. doi:10.1007/s00181-017-1245-2
   Web of Science ®Google Scholar
• Abraham ER, Mendes dos Reis JG, Vendrametto O, et al. Time series prediction with artificial neural networks: an analysis using brazilian soybean production. Agriculture. 2020;10(10):475. doi:10.3390/agriculture10100475
   Web of Science ®Google Scholar
• Xu X, Zhang Y. Network analysis of corn cash price comovements. Mach Learning Appl. 2021;6:100140. doi:10.1016/j.mlwa.2021.100140
   Google Scholar
• Liu Z, Zhu S, Wang Y, et al. Thread steel price index prediction in China based on arima model. In: LISS 2014. Springer; 2015. p. 609–614. doi:10.1007/978-3-662-43871-8_88
   Google Scholar
• Zola P, Carpita M. Forecasting the steel product prices with the arima model. Stat Appl. 2016;14:1. doi:10.1400/250432
   Google Scholar
• Kapl M, Müller WG. Prediction of steel prices: a comparison between a conventional regression model and mssa. Stat Interface. 2010;3(3):369–375. doi:10.4310/SII.2010.v3.n3.a10
   Web of Science ®Google Scholar
• Adli KA. Forecasting steel prices using arimax model: a case study of turkey. Int J Bus Manag Technol. 2020;4(5):62–68.
   Google Scholar
• Supattana N. Steel price index forecasting using arima and arimax model. Natl Inst Develop Admin. 2014;1.
   Google Scholar
• Tcha M, Kim PJ. Steel price projections, in: The Economics of the East Asia Steel Industries, Routledge, 2019, p. 225–256.
   Google Scholar
• Ming-Tao C, Bo-Ching H. An analysis of the relationship between forward freight agreements and steel price index: an application of the vector arma model. Afr J Bus Manag. 2010;4:1149–1154.
   Google Scholar
• Xiarchos IM. Steel: Price links between primary and scrap market. Technical Report. 2005. doi:10.22004/ag.econ.35655
   Google Scholar
• Chou M-T. Dynamic economic relations among steel price indices. J Mar Sci Technol. 2016;24:3. doi:10.6119/JMST-016-0504-1
   Web of Science ®Google Scholar
• Faghih SAM, Kashani H. Forecasting construction material prices using vector error correction model. J Constr Eng Manag. 2018;144(8):04018075. doi:10.1061/(ASCE)CO.1943-7862.0001528
   Web of Science ®Google Scholar
• Jiang H, Xu Y, Liu C. Market effects on forecasting construction prices using vector error correction models. Int J Construct Manage. 2014;14:2101–112. doi:10.1080/15623599.2014.899128
   Google Scholar
• Kim S, Abediniangerabi B, Shahandashti M. ASCE, pipeline construction cost forecasting using multivariate time series methods. J Pipe Syst Eng Pract. 2021;12(3):04021026. doi:10.1061/(ASCE)PS.1949-1204.0000553
   Web of Science ®Google Scholar
• Bin D. The empirical study on dynamic relationship between domestic and global steel price. In: 2007 International Conference on Wireless Communications, Networking and Mobile Computing. IEEE; 2007. p. 4347–4350. doi:10.1109/WICOM.2007.1072
   Google Scholar
• Liu Y, Yang C, Huang K, et al. Non-ferrous metals price forecasting based on variational mode decomposition and lstm network. Knowl Based Syst. 2020;188:105006. doi:10.1016/j.knosys.2019.105006
   Web of Science ®Google Scholar
• Tuo J, Zhang F. Modelling the iron ore price index: a new perspective from a hybrid data reconstructed eemd-goru model. J Manag Sci Eng. 2020;5:212–225. doi:10.1016/j.jmse.2020.08.003
   Web of Science ®Google Scholar
• Wang Z-X, Zhao Y-F, He L-Y. Forecasting the monthly iron ore import of China using a model combining empirical mode decomposition non-linear autoregressive neural network, and autoregressive integrated moving average. Appl Soft Comput. 2020;94:106475. doi:10.1016/j.asoc.2020.106475
   Web of Science ®Google Scholar
• Wang J, Li X. A combined neural network model for commodity price forecasting with ssa. Soft Comput. 2018;22:165323–5333. doi:10.1007/s00500-018-3023-2
   Web of Science ®Google Scholar
• Ou T-Y, Cheng C-Y, Chen P-J, et al. Dynamic cost forecasting model based on extreme learning machine-a case study in steel plant. Comput Ind Eng. 2016;101:544–553. doi:10.1016/j.cie.2016.09.012
   Web of Science ®Google Scholar
• Raju S, Sarker A, Das A, et al. An approach for demand forecasting in steel industries using ensemble learning. Complexity. 2022;2022:1–19. doi:10.1155/2022/9928836
   Web of Science ®Google Scholar
• Xu X. The rolling causal structure between the Chinese stock index and futures. Financ Mark Portf Manag. 2017;31(4):491–509. doi:10.1007/s11408-017-0299-7
   Google Scholar
• Xu X. Contemporaneous causal orderings of csi300 and futures prices through directed acyclic graphs. Econ Bull. 2019;39:2052–2077. http://www.accessecon.com/Pubs/EB/2019/Volume39/EB-19-V39-I3-P192.pdf.
   Web of Science ®Google Scholar
• Xu X, Zhang Y. Contemporaneous causality among one hundred Chinese cities. Empir Econ. 2022;63(4):2315–2329. doi:10.1007/s00181-021-02190-5
   Web of Science ®Google Scholar
• Xu X, Zhang Y. Network analysis of comovements among newly-built residential house price indices of seventy Chinese cities. Int J Hous Mark Anal. 2022. doi:10.1108/IJHMA-09-2022-0134
   Web of Science ®Google Scholar
• Dickey DA, Fuller WA. Likelihood ratio statistics for autoregressive time series with a unit root. Econ: J Econ Soc. 1981;1:1057–1072. doi:10.2307/1912517
   Web of Science ®Google Scholar
• Phillips PC, Perron P. Testing for a unit root in time series regression. Biometrika. 1988;75(2):335–346. doi:10.1093/biomet/75.2.335
   Web of Science ®Google Scholar
• Kwiatkowski D, Phillips PC, Schmidt P, et al. Testing the null hypothesis of stationarity against the alternative of a unit root. J Econom. 1992;54(1-3):159–178. doi:10.1016/0304-4076(92)90104-Y
   Web of Science ®Google Scholar
• Levenberg K. A method for the solution of certain non-linear problems in least squares. Quart Appl Math. 1944;2(2):164–168.
   Google Scholar
• Marquardt DW. An algorithm for least-squares estimation of nonlinear parameters. J Soc Indus Appl Math. 1963;11:2431–441. doi:10.1137/0111030
   Google Scholar
• Møller MF. A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 1993;6(4):525–533. doi:10.1016/S0893-6080(05)80056-5
   Web of Science ®Google Scholar
• Xu X, Zhang Y. Soybean and soybean oil price forecasting through the nonlinear autoregressive neural network (narnn) and narnn with exogenous inputs (narnn–x). Intell Syst Appl. 2022;13:200061. doi:10.1016/j.iswa.2022.200061
   Google Scholar
• Doan CD, Liong S-Y. Generalization for multilayer neural network bayesian regularization or early stopping. In: Proceedings of Asia Pacific Association of Hydrology and Water Resources 2nd Conference. 2004. p. 5–8.
   Google Scholar
• Kayri M. Predictive abilities of bayesian regularization and levenberg–marquardt algorithms in artificial neural networks: a comparative empirical study on social data. Math Comput Appl. 2016;21:20. doi:10.3390/mca21020020
   Google Scholar
• Xu X, Zhang Y. House price forecasting with neural networks. Intell Syst Appl. 2021;12:200052. doi:10.1016/j.iswa.2021.200052
   Google Scholar
• Khan TA, Alam M, Shahid Z, et al. Comparative performance analysis of levenberg-marquardt, bayesian regularization and scaled conjugate gradient for the prediction of flash floods. J Inform Commun Tech Robot Appl. 2019;10:52–58. http://jictra.com.pk/index.php/jictra/article/view/188/112
   Google Scholar
• Xu X, Zhang Y. Second-hand house price index forecasting with neural networks. J Prop Res. 2022;39(3):215–236. doi:10.1080/09599916.2021.1996446
   Web of Science ®Google Scholar
• Selvamuthu D, Kumar V, Mishra A. Indian stock market prediction using artificial neural networks on tick data. Finan Innov. 2019;5(1):16. doi:10.1186/s40854-019-0131-7
   Web of Science ®Google Scholar
• Xu X, Zhang Y. Rent index forecasting through neural networks. J Econ Stud. 2022;49(8):1321–1339. doi:10.1108/JES-06-2021-0316
   Web of Science ®Google Scholar
• Xu X, Zhang Y. Retail property price index forecasting through neural networks. J Real Estate Portf Manag. 2022;1–28. doi:10.1080/10835547.2022.2110668
   Google Scholar
• Baghirli O. Comparison of lavenberg-marquardt, scaled conjugate gradient and bayesian regularization backpropagation algorithms for multistep ahead wind speed forecasting using multilayer perceptron feedforward neural network. 2015.
   Google Scholar
• Xu X, Zhang Y. Residential housing price index forecasting via neural networks. Neural Comput Appl. 2022;34(17):14763–14776. doi:10.1007/s00521-022-07309-y
   Web of Science ®Google Scholar
• Al Bataineh A, Kaur D. 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; 2018. p. 174–178. doi:10.1109/NAECON.2018.8556738
   Google Scholar
• Xu X, Zhang Y. Coking coal futures price index forecasting with the neural network. Min Econ. 2023;36(2):349–359. doi:10.1007/s13563-022-00311-9
   Web of Science ®Google Scholar
• Paluszek M, Thomas S. Practical MATLAB deep learning: a project-based approach. New York, NC: Apress; 2020. https://link.springer.com/content/pdf/10.1007/978-1-4842-5124-9.pdf.
   Google Scholar
• Xu X, Zhang Y. A high-frequency trading volume prediction model using neural networks. Decis Anal J. 2023;7:100235. doi:10.1016/j.dajour.2023.100235
   Google Scholar
• Hagan MT, Menhaj MB. Training feedforward networks with the marquardt algorithm. IEEE Trans Neural Netw. 1994;5(6):989–993. doi:10.1109/72.329697
   PubMed Web of Science ®Google Scholar
• Hagan MT, Demuth HB, Beale M. Neural network design. Boston, MA: PWS Publishing Co; 1997.
   Google Scholar
• Xu X, Zhang Y. Forecasting the total market value of a shares traded in the shenzhen stock exchange via the neural network. Econom Bull. 2022.
   Google Scholar
• Batra D. Comparison between Levenberg-Marquardt and scaled conjugate gradient training algorithms for image compression using mlp. Int J Imag Proces. 2014;8:412–422.
   Google Scholar
• Xu X, Zhang Y. Thermal coal price forecasting via the neural network. Intel Syst Appl. 2022;14:200084. doi:10.1016/j.iswa.2022.200084
   Google Scholar
• Brock WA, Scheinkman JA, Dechert WD, et al. A test for independence based on the correlation dimension. Econom Rev. 1996;15(3):197–235. doi:10.1080/07474939608800353
   Google Scholar
• Dergiades T, Martinopoulos G, Tsoulfidis L. Energy consumption and economic growth: parametric and non-parametric causality testing for the case of greece. Energy Econ. 2013;36:686–697. doi:10.1016/j.eneco.2012.11.017
   Web of Science ®Google Scholar
• Fujihara RA, Mougoué M. An examination of linear and nonlinear causal relationships between price variability and volume in petroleum futures markets. J Futures Markets: Futures Options Other Deriv Products. 1997;17:385–416. doi:10.1002/(SICI)1096-9934(199706)17:4<385::AID-FUT2¿3.0.CO;2-D
   Web of Science ®Google Scholar
• Diebold FX, Mariano RS. Comparing predictive accuracy. J Bus Econ Stat. 2002;20(1):134–144. doi:10.2307/1392185
   Web of Science ®Google Scholar
• Harvey D, Leybourne S, Newbold P. Testing the equality of prediction mean squared errors. Int J Forecast. 1997;13(2):281–291. doi:10.1016/S0169-2070(96)00719-4
   Web of Science ®Google Scholar
• Kano Y, Shimizu S. Causal inference using nonnormality, In Proceedings of the international symposium on science of modeling, the 30th anniversary of the information criterion. 2003. p. 261–270.
   Google Scholar
• Shimizu S, Hoyer PO, Hyvärinen A, et al. A linear non-Gaussian acyclic model for causal discovery. J Mach Learn Res. 2006;7:2003–2030. https://www.jmlr.org/papers/volume7/shimizu06a/shimizu06a.pdf?ref=https://codemonkey.link
   Web of Science ®Google Scholar
• Xu X, Zhang Y. Contemporaneous causality among residential housing prices of ten major Chinese cities. Int J Housing Markets Anal. 2022. doi:10.1108/IJHMA-03-2022-0039
   Web of Science ®Google Scholar
• Shimizu S, Kano Y. Use of non-normality in structural equation modeling: application to direction of causation. J Stat Plan Inference. 2008;138(11):3483–3491. doi:10.1016/j.jspi.2006.01.017
   Web of Science ®Google Scholar
• Shimizu S, Inazumi T, Sogawa Y, et al. Directlingam A direct method for learning a linear non-Gaussian structural equation model. J Mach Learn Res. 2011;12:1225–1248. https://www.jmlr.org/papers/volume12/shimizu11a/shimizu11a.pdf
   Web of Science ®Google Scholar
• Xu X. Causality and price discovery in US corn markets: an application of error correction modeling and directed acyclic graphs. 2014. doi:10.22004/ag.econ.169806
   Google Scholar
• Bessler DA, Wang Z. D-separation, forecasting, and economic science: a conjecture. Theory Decis. 2012;73(2):295–314. doi:10.1007/s11238-012-9305-8
   Web of Science ®Google Scholar