{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T06:13:58Z","timestamp":1770272038309,"version":"3.49.0"},"reference-count":61,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Humanity and Social Science Foundation of the Ministry of Education of China","award":["18YJA630037"],"award-info":[{"award-number":["18YJA630037"]}]},{"name":"Humanity and Social Science Foundation of the Ministry of Education of China","award":["21YJA630054"],"award-info":[{"award-number":["21YJA630054"]}]},{"name":"Humanity and Social Science Foundation of the Ministry of Education of China","award":["2024C350470"],"award-info":[{"award-number":["2024C350470"]}]},{"name":"Zhejiang Province Soft Science Research Program Project","award":["18YJA630037"],"award-info":[{"award-number":["18YJA630037"]}]},{"name":"Zhejiang Province Soft Science Research Program Project","award":["21YJA630054"],"award-info":[{"award-number":["21YJA630054"]}]},{"name":"Zhejiang Province Soft Science Research Program Project","award":["2024C350470"],"award-info":[{"award-number":["2024C350470"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Current research on futures price prediction focuses on the autocorrelation of historical prices, yet the resulting predictions often suffer from issues of inaccuracy and lag. This paper uses Chinese corn futures as the subject of study. First, we identify key influencing factors, such as Chinese soybean futures, U.S. soybean futures, and the U.S.-China exchange rate, that exhibit \u2018predictive causality\u2019 with corn futures prices through the Granger causality test. We then apply the sample convolution and interaction network (SCINet) to perform both single-step and multi-step predictions of futures prices. The experimental results show that incorporating key influencing factors significantly improves prediction accuracy. For instance, in the single-step prediction, combining historical prices with Chinese soybean futures prices reduces the MAE and RMSE values by 5.12% and 3.45%, respectively, compared to using historical prices alone. Furthermore, the SCINet model outperforms traditional models such as temporal convolutional networks (TCN), gated recurrent units (GRU), and long short-term memory (LSTM) networks when based solely on historical prices. This study validates the effectiveness of key influencing factors in forecasting Chinese corn futures prices and demonstrates the advantages of the SCINet model in futures price prediction. The findings provide valuable insights for optimising the agricultural futures market and enhancing the ability to predict price risks.<\/jats:p>","DOI":"10.3390\/info15120817","type":"journal-article","created":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T10:54:20Z","timestamp":1734605660000},"page":"817","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Study of Futures Price Forecasting with a Focus on the Role of Different Economic Markets"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3346-3148","authenticated-orcid":false,"given":"Yongxiang","family":"Wang","sequence":"first","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou 311300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingyang","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Informatics, University of G\u00f6ttingen, 37073 G\u00f6ttingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanrong","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou 311300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8175-264X","authenticated-orcid":false,"given":"Hongjiu","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou 311300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1007\/s12571-022-01288-7","article-title":"Global Maize Production, Consumption and Trade: Trends and R&D Implications","volume":"14","author":"Erenstein","year":"2022","journal-title":"Food Secur."},{"key":"ref_2","first-page":"200111","article-title":"Can Corn Stove Bioethanol Production Substantially Contribute to China\u2019s Carbon Neutrality Ambition?","volume":"15","author":"Fu","year":"2022","journal-title":"Resour. Conserv. Recycl. Adv."},{"key":"ref_3","first-page":"200","article-title":"Nonlinear Analysis and Prediction of Soybean Futures","volume":"67","author":"Yin","year":"2021","journal-title":"Agric. Econ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"110822","DOI":"10.1016\/j.chaos.2021.110822","article-title":"Ensemble Forecasting for Product Futures Prices Using Variational Mode Decomposition and Artificial Neural Networks","volume":"146","author":"Liu","year":"2021","journal-title":"Chaos Solitons Fractals"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1016\/j.procs.2018.08.215","article-title":"Arima Model for Forecasting the Price of Medium Quality Rice to Anticipate Price Fluctuations","volume":"135","author":"Ohyver","year":"2018","journal-title":"Proc. Procedia Comput. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"118787","DOI":"10.1016\/j.energy.2020.118787","article-title":"Forcasting of Energy Futures Market and Synchronization Based on Stochastic Gated Recurrent Unit Model","volume":"213","author":"Li","year":"2020","journal-title":"Energy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"070204:1","DOI":"10.1007\/s11432-018-9714-5","article-title":"Pigeon-Inspired Optimization and Extreme Learning Machine via Wavelet Packet Analysis for Predicting Bulk Commodity Futures Prices","volume":"62","author":"Jiang","year":"2019","journal-title":"Sci. China Inf. Sci."},{"key":"ref_8","unstructured":"Heaton, J.B., Polson, N.G., and Witte, J.H. (2016). Deep Learning in Finance. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1016\/j.procs.2020.03.136","article-title":"Crude Oil Price Prediction Using Artificial Neural Network","volume":"170","author":"Gupta","year":"2020","journal-title":"Proc. Procedia Comput. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Du, Y., Chen, K., Chen, S., and Yin, K. (2022). Prediction of Carbon Emissions Trading Price in Fujian Province: Based on BP Neural Network Model. Front. Energy Res., 10.","DOI":"10.3389\/fenrg.2022.939602"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"109536","DOI":"10.1016\/j.buildenv.2022.109536","article-title":"Attention-LSTM Architecture Combined with Bayesian Hyperparameter Optimization for Indoor Temperature Prediction","volume":"224","author":"Jiang","year":"2022","journal-title":"Build. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"127799","DOI":"10.1016\/j.energy.2023.127799","article-title":"An Imputation and Decomposition Algorithms Based Integrated Approach with Bidirectional LSTM Neural Network for Wind Speed Prediction","volume":"278","author":"Sareen","year":"2023","journal-title":"Energy"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.neucom.2018.12.016","article-title":"Traffic Flow Prediction Using LSTM with Feature Enhancement","volume":"332","author":"Yang","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"117370","DOI":"10.1016\/j.eswa.2022.117370","article-title":"China\u2019s Commercial Bank Stock Price Prediction Using a Novel K-Means-LSTM Hybrid Approach","volume":"202","author":"Chen","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101414","DOI":"10.1016\/j.resourpol.2019.101414","article-title":"Predictive Analytics of the Copper Spot Price by Utilizing Complex Network and Artificial Neural Network Techniques","volume":"63","author":"Wang","year":"2019","journal-title":"Resour. Policy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.apenergy.2016.12.134","article-title":"Multi-Step Ahead Electricity Price Forecasting Using a Hybrid Model Based on Two-Layer Decomposition Technique and BP Neural Network Optimized by Firefly Algorithm","volume":"190","author":"Wang","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/j.renene.2022.08.146","article-title":"Is Ethanol Production Responsible for the Increase in Corn Prices?","volume":"199","author":"Kocak","year":"2022","journal-title":"Renew. Energy"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1111\/agec.12350","article-title":"Decompositions of Corn Price Effects: Implications for Feed Grain Demand and Livestock Supply","volume":"48","author":"Suh","year":"2017","journal-title":"Agric. Econ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"106120","DOI":"10.1016\/j.compag.2021.106120","article-title":"Corn Cash Price Forecasting with Neural Networks","volume":"184","author":"Xu","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1400","DOI":"10.1016\/j.eneco.2012.06.018","article-title":"The Effect of Ethanol Listing on Corn Prices: Evidence from Spot and Futures Markets","volume":"34","author":"Demirer","year":"2012","journal-title":"Energy Econ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.ijepes.2014.03.007","article-title":"A Time Series Spot Price Forecast Model for the Nord Pool Market","volume":"61","author":"Kristiansen","year":"2014","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"107089","DOI":"10.1016\/j.eneco.2023.107089","article-title":"Forecasting Crude Oil Futures Price Using Machine Learning Methods: Evidence from China","volume":"127","author":"Guo","year":"2023","journal-title":"Energy Econ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"424","DOI":"10.2307\/1912791","article-title":"Investigating Causal Relations by Econometric Models and Cross-Spectral Methods","volume":"37","author":"Granger","year":"1969","journal-title":"Econometrica"},{"key":"ref_24","first-page":"5816","article-title":"Scinet: Time Series Modeling and Forecasting with Sample Convolution and Interaction","volume":"35","author":"Liu","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.ins.2022.05.088","article-title":"Multi-Step-Ahead Stock Price Index Forecasting Using Long Short-Term Memory Model with Multivariate Empirical Mode Decomposition","volume":"607","author":"Deng","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1109\/TPWRS.2002.804943","article-title":"ARIMA Models to Predict Next-Day Electricity Prices","volume":"18","author":"Contreras","year":"2003","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"979","DOI":"10.4028\/www.scientific.net\/AMR.798-799.979","article-title":"Application of ARIMA Model in Short-Term Prediction of International Crude Oil Price","volume":"798","author":"Xiang","year":"2013","journal-title":"Adv. Mater. Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1016\/j.eneco.2005.07.001","article-title":"Modeling and Forecasting Cointegrated Relationships among Heavy Oil and Product Prices","volume":"27","author":"Lanza","year":"2005","journal-title":"Energy Econ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yi, A., Yang, M., and Li, Y. (2021). Macroeconomic Uncertainty and Crude Oil Futures Volatility\u2013Evidence from China Crude Oil Futures Market. Front. Environ. Sci., 9.","DOI":"10.3389\/fenvs.2021.636903"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2913","DOI":"10.1016\/j.neucom.2007.01.009","article-title":"An Investigation and Comparison of Artificial Neural Network and Time Series Models for Chinese Food Grain Price Forecasting","volume":"70","author":"Zou","year":"2007","journal-title":"Neurocomputing"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.resourpol.2015.03.004","article-title":"Forecasting the COMEX Copper Spot Price by Means of Neural Networks and ARIMA Models","volume":"45","year":"2015","journal-title":"Resour. Policy"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.eswa.2019.03.029","article-title":"CNNpred: CNN-Based Stock Market Prediction Using a Diverse Set of Variables","volume":"129","author":"Hoseinzade","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.energy.2018.12.016","article-title":"Crude Oil Price Prediction Model with Long Short Term Memory Deep Learning Based on Prior Knowledge Data Transfer","volume":"169","author":"Cen","year":"2019","journal-title":"Energy"},{"key":"ref_34","unstructured":"Bai, S., Kolter, J.Z., and Koltun, V. (2018). An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"120452","DOI":"10.1016\/j.apenergy.2022.120452","article-title":"Forecasting Carbon Prices Based on Real-Time Decomposition and Causal Temporal Convolutional Networks","volume":"331","author":"Li","year":"2023","journal-title":"Appl. Energy"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"106076","DOI":"10.1016\/j.ssci.2023.106076","article-title":"The Impact of COVID-19 Pandemic on Construction Safety in China and the U.S.: A Comparative Study","volume":"161","author":"Duan","year":"2023","journal-title":"Saf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1007\/s12206-023-0234-y","article-title":"Improved CEEMDAN-Based Aero-Engine Gas-Path Parameter Forecasting Using SCINet","volume":"37","author":"Song","year":"2023","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"103582","DOI":"10.1016\/j.apor.2023.103582","article-title":"Regional Forecasting of Significant Wave Height and Mean Wave Period Using EOF-EEMD-SCINet Hybrid Model","volume":"136","author":"Ding","year":"2023","journal-title":"Appl. Ocean Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103915","DOI":"10.1016\/j.frl.2023.103915","article-title":"Predicting Natural Gas Futures\u2019 Volatility Using Climate Risks","volume":"55","author":"Guo","year":"2023","journal-title":"Financ. Res. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"107734","DOI":"10.1016\/j.asoc.2021.107734","article-title":"A Novel Hybrid Method for Direction Forecasting and Trading of Apple Futures","volume":"110","author":"Deng","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lu, Y., Teng, Y., Zhang, Q., and Dai, J. (2023). Prediction Model for the Chemical Futures Price Using Improved Genetic Algorithm Based Long Short-Term Memory. Processes, 11.","DOI":"10.3390\/pr11010238"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"109723","DOI":"10.1016\/j.asoc.2022.109723","article-title":"Forecasting Crude Oil Futures Prices Using BiLSTM-Attention-CNN Model with Wavelet Transform","volume":"130","author":"Lin","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.eswa.2016.02.006","article-title":"Computational Intelligence and Financial Markets: A Survey and Future Directions","volume":"55","author":"Cavalcante","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"105739","DOI":"10.1016\/j.asoc.2019.105739","article-title":"A Hybrid VMD\u2013BiGRU Model for Rubber Futures Time Series Forecasting","volume":"84","author":"Zhu","year":"2019","journal-title":"Appl. Soft Comput. J."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1016\/j.apenergy.2017.01.076","article-title":"Forecasting Carbon Price Using Empirical Mode Decomposition and Evolutionary Least Squares Support Vector Regression","volume":"191","author":"Zhu","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1109\/TCSS.2021.3084847","article-title":"A New Hybrid VMD-ICSS-BiGRU Approach for Gold Futures Price Forecasting and Algorithmic Trading","volume":"8","author":"Li","year":"2021","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"101632","DOI":"10.1016\/j.asieco.2023.101632","article-title":"The Impact of Natural Disaster Risk on the Return of Agricultural Futures","volume":"87","author":"Hua","year":"2023","journal-title":"J. Asian Econ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.dsm.2022.09.002","article-title":"China Futures Price Forecasting Based on Online Search and Information Transfer","volume":"5","author":"Liang","year":"2022","journal-title":"Data Sci. Manag."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.ijforecast.2020.02.002","article-title":"A Novel Text-Based Framework for Forecasting Agricultural Futures Using Massive Online News Headlines","volume":"38","author":"Li","year":"2022","journal-title":"Int. J. Forecast."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"100111","DOI":"10.1016\/j.jcomm.2019.100111","article-title":"Dynamics of Volatility Spillover in Commodity Markets: Linking Crude Oil to Agriculture","volume":"20","author":"Dahl","year":"2020","journal-title":"J. Commod. Mark."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1016\/j.ijforecast.2016.01.002","article-title":"Forecasting Food Prices: The Case of Corn, Soybeans and Wheat","volume":"32","author":"Ahumada","year":"2016","journal-title":"Int. J. Forecast."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"103567","DOI":"10.1016\/j.resourpol.2023.103567","article-title":"Dependence and Risk Management of Portfolios of Metals and Agricultural Commodity Futures","volume":"82","author":"Hanif","year":"2023","journal-title":"Resour. Policy"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"104128","DOI":"10.1016\/j.resourpol.2023.104128","article-title":"Time Varying Connectedness between Foreign Exchange Markets and Crude Oil Futures Prices","volume":"86","author":"Lu","year":"2023","journal-title":"Resour. Policy"},{"key":"ref_54","first-page":"11","article-title":"Could Exist a Causality Between the Most Traded Commodities and Futures Commodity Prices in the Agricultural Market?","volume":"14","year":"2022","journal-title":"Agris On-Line Pap. Econ. Inform."},{"key":"ref_55","unstructured":"Xu, X. (2015). Causality, Price Discovery, and Price Forecasts: Evidence from U.S. Corn Cash and Futures Markets. [Ph.D. Thesis, North Carolina State University]."},{"key":"ref_56","first-page":"20160006","article-title":"Linear and Nonlinear Causality between Corn Cash and Futures Prices","volume":"16","author":"Xu","year":"2018","journal-title":"J. Agric. Food Ind. Organ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2455","DOI":"10.1080\/02664763.2017.1423044","article-title":"Causal Structure among US Corn Futures and Regional Cash Prices in the Time and Frequency Domain","volume":"45","author":"Xu","year":"2018","journal-title":"J. Appl. Stat."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.neucom.2020.08.011","article-title":"Explaining the Black-Box Model: A Survey of Local Interpretation Methods for Deep Neural Networks","volume":"419","author":"Liang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_59","unstructured":"Xu, X., and Thurman, W.N. (2015, January 20\u201321). Using Local Information to Improve Short-Run Corn Cash Price Forecasts. Proceedings of the NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management, St. Louis, MO, USA."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1889","DOI":"10.1007\/s00181-017-1322-6","article-title":"Cointegration and Price Discovery in US Corn Cash and Futures Markets","volume":"55","author":"Xu","year":"2018","journal-title":"Empir. Econ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s11408-019-00330-7","article-title":"Price Dynamics in Corn Cash and Futures Markets: Cointegration, Causality, and Forecasting through a Rolling Window Approach","volume":"33","author":"Xu","year":"2019","journal-title":"Financ. Mark. Portf. Manag."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/12\/817\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:55:48Z","timestamp":1760115348000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/12\/817"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,19]]},"references-count":61,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["info15120817"],"URL":"https:\/\/doi.org\/10.3390\/info15120817","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,19]]}}}