{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T17:39:40Z","timestamp":1781804380122,"version":"3.54.5"},"reference-count":52,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,9,12]],"date-time":"2017-09-12T00:00:00Z","timestamp":1505174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation, China","award":["71301153"],"award-info":[{"award-number":["71301153"]}]},{"name":"the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry of China"},{"name":"the Science Foundation of Mineral Resource Strategy and Policy Research Center, China University of Geosciences","award":["H2017011B"],"award-info":[{"award-number":["H2017011B"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Agricultural commodity futures prices play a significant role in the change tendency of these spot prices and the supply\u2013demand relationship of global agricultural product markets. Due to the nonlinear and nonstationary nature of this kind of time series data, it is inevitable for price forecasting research to take this nature into consideration. Therefore, we aim to enrich the existing research literature and offer a new way of thinking about forecasting agricultural commodity futures prices, so that four hybrid models are proposed based on the back propagation neural network (BPNN) optimized by the particle swarm optimization (PSO) algorithm and four decomposition methods: empirical mode decomposition (EMD), wavelet packet transform (WPT), intrinsic time-scale decomposition (ITD) and variational mode decomposition (VMD). In order to verify the applicability and validity of these hybrid models, we select three futures prices of wheat, corn and soybean to conduct the experiment. The experimental results show that (1) all the hybrid models combined with decomposition technique have a better performance than the single PSO\u2013BPNN model; (2) VMD contributes the most in improving the forecasting ability of the PSO\u2013BPNN model, while WPT ranks second; (3) ITD performs better than EMD in both cases of corn and soybean; and (4) the proposed models perform well in the forecasting of agricultural commodity futures prices.<\/jats:p>","DOI":"10.3390\/a10030108","type":"journal-article","created":{"date-parts":[[2017,9,12]],"date-time":"2017-09-12T10:40:04Z","timestamp":1505212804000},"page":"108","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Performance Analysis of Four Decomposition-Ensemble Models for One-Day-Ahead Agricultural Commodity Futures Price Forecasting"],"prefix":"10.3390","volume":"10","author":[{"given":"Deyun","family":"Wang","sequence":"first","affiliation":[{"name":"School of Economics and Management, China University of Geosciences, Wuhan 430074, China"},{"name":"Mineral Resource Strategy and Policy Research Center, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenqiang","family":"Yue","sequence":"additional","affiliation":[{"name":"School of Economics and Management, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuai","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Economics and Management, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Economics and Management, China University of Geosciences, Wuhan 430074, China"},{"name":"Mineral Resource Strategy and Policy Research Center, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,12]]},"reference":[{"key":"ref_1","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 China food grain price forecasting","volume":"70","author":"Zou","year":"2007","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.rse.2005.09.010","article-title":"Image masking for crop yield forecasting using AVHRR NDVI time series imagery","volume":"99","author":"Kastens","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.agrformet.2012.08.010","article-title":"Pre-harvest forecasting of county wheat yield and wheat quality using weather information","volume":"168","author":"Lee","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.rse.2013.10.027","article-title":"An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States","volume":"141","author":"Johnson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4971","DOI":"10.1016\/j.enpol.2011.06.016","article-title":"Is there co-movement of agricultural commodities futures prices and crude oil?","volume":"39","author":"Natanelov","year":"2011","journal-title":"Energy Policy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3930","DOI":"10.1016\/j.physa.2012.02.029","article-title":"Cross-correlations between agricultural commodity futures markets in the US and China","volume":"391","author":"Li","year":"2012","journal-title":"Physica A"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.eneco.2013.06.013","article-title":"Do energy prices stimulate food price volatility? Examining volatility transmission between US oil, ethanol and corn markets","volume":"40","author":"Gardebroek","year":"2013","journal-title":"Energy Econ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.pacfin.2013.10.007","article-title":"Asymmetric information and volatility forecasting in commodity futures markets","volume":"26","author":"Liu","year":"2014","journal-title":"Pac.-Basin Financ. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1016\/j.econmod.2013.09.036","article-title":"Volatility transmission in agricultural futures markets","volume":"36","author":"Beckmann","year":"2014","journal-title":"Econ. Model."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.jempfin.2015.07.003","article-title":"Risk-adjusted implied volatility and its performance in forecasting realized volatility in corn futures prices","volume":"34","author":"Wu","year":"2015","journal-title":"J. Empir. Financ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.jempfin.2016.05.005","article-title":"Smooth volatility shifts and spillover in U.S. crude oil and corn futures markets","volume":"38","author":"Teterin","year":"2016","journal-title":"J. Empir. Financ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.eneco.2015.11.018","article-title":"Volatility linkages between energy and agricultural commodity prices","volume":"54","author":"Cabrera","year":"2016","journal-title":"Energy Econ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.econmod.2014.11.027","article-title":"Spatial price transmission on agricultural commodity markets under different volatility regimes","volume":"52","author":"Ganneval","year":"2016","journal-title":"Econ. Model."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.ijforecast.2016.08.002","article-title":"Realized volatility forecasting of agricultural com-modity futures using HAR model with time-varying sparsity","volume":"33","author":"Tian","year":"2017","journal-title":"Int. J. Forecast."},{"key":"ref_15","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_16","first-page":"389","article-title":"Adaptive market efficiency of agricultural commodity futures contracts","volume":"60","author":"Arellano","year":"2015","journal-title":"Contad. Adm."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/j.foodpol.2011.07.001","article-title":"A quantitative analysis of trade policy responses to higher world agricultural commodity prices","volume":"36","author":"Yu","year":"2011","journal-title":"Food Policy"},{"key":"ref_18","first-page":"132","article-title":"Modeling and forecasting volatility in the global food commodity prices","volume":"57","author":"Onour","year":"1996","journal-title":"Agric. Econ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1002\/(SICI)1096-9934(199908)19:5<603::AID-FUT6>3.0.CO;2-U","article-title":"A reappraisal of the forecasting performance of corn and soybean new crop futures","volume":"19","author":"Zulauf","year":"1999","journal-title":"J. Futures Mark."},{"key":"ref_20","first-page":"551","article-title":"Nonlinearities in the price behaviour of agricultural products: The case of cotton","volume":"9","author":"Zafeiriou","year":"2011","journal-title":"J. Agric. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.knosys.2015.01.002","article-title":"A combination method for interval forecasting of agricultural commodity futures prices","volume":"77","author":"Xiong","year":"2015","journal-title":"Knowl.-Based Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.econlet.2014.11.008","article-title":"Log versus level in VAR forecasting: 42 million empirical answers-Expect the unexpected","volume":"126","author":"Mayr","year":"2015","journal-title":"Econ. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1016\/j.econmod.2015.10.016","article-title":"Does the vector error correction model perform better than others in forecasting stock price? An application of residual income valuation theory","volume":"52","author":"Kuo","year":"2016","journal-title":"Econ. Model."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1016\/j.energy.2016.10.068","article-title":"Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization","volume":"116","author":"Sen","year":"2016","journal-title":"Energy"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.najef.2015.10.001","article-title":"VIX forecasting and variance risk premium: A new GARCH approach","volume":"34","author":"Liu","year":"2015","journal-title":"N. Am. J. Econ. Financ."},{"key":"ref_26","first-page":"14178","article-title":"Enhanced stock price variation prediction via DOE and BPNN-based optimization","volume":"38","author":"Hsieh","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.ijepes.2014.01.023","article-title":"Mid-term electricity market clearing price forecasting: A multiple SVM approach","volume":"58","author":"Yan","year":"2014","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1016\/j.eswa.2009.08.019","article-title":"Power load forecasting using support vector machine and ant colony optimization","volume":"37","author":"Niu","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.jocs.2013.11.004","article-title":"Enhanced artificial bee colony for training least squares support vector machines in commodity price forecasting","volume":"5","author":"Mustaffa","year":"2014","journal-title":"J. Comput Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1439","DOI":"10.1016\/j.ijleo.2013.09.017","article-title":"Real estate price forecasting based on SVM optimized by PSO","volume":"125","author":"Wang","year":"2014","journal-title":"Optik"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.jocs.2015.11.011","article-title":"Intraday stock price forecasting based on variational mode decomposition","volume":"12","author":"Lahmiri","year":"2016","journal-title":"J. Comput. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.ins.2015.01.029","article-title":"Forecasting interval time series using a fully complex valued RBF neural network with DPSO and PSO algorithms","volume":"305","author":"Xiong","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1016\/j.asoc.2016.07.053","article-title":"Modelling a combined method based on ANFIS and neural network improved by DE algorithm: A case study for short-term electricity demand forecasting","volume":"49","author":"Yang","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.neucom.2016.09.027","article-title":"A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm","volume":"221","author":"Hu","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.apenergy.2015.07.025","article-title":"A decomposition-ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting","volume":"156","author":"Yu","year":"2015","journal-title":"Appl. Energy"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.omega.2004.07.024","article-title":"A hybrid ARIMA and support vector machines model in stock price forecasting","volume":"33","author":"Pai","year":"2005","journal-title":"Omega"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2664","DOI":"10.1016\/j.asoc.2010.10.015","article-title":"A novel hybridization of artificial neural networks and ARIMA models for time series forecasting","volume":"11","author":"Khashei","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.procs.2015.04.167","article-title":"Time Series Forecasting using Hybrid ARIMA and ANN Models based on DWT Decomposition","volume":"48","author":"Khandelwal","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/j.apenergy.2015.01.038","article-title":"The study and application of a novel hybrid forecasting model\u2014A case study of wind speed forecasting in China","volume":"143","author":"Wang","year":"2015","journal-title":"Appl. Energy"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2623","DOI":"10.1016\/j.eneco.2008.05.003","article-title":"Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm","volume":"30","author":"Yu","year":"2008","journal-title":"Energy Econ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.ijepes.2014.06.010","article-title":"Interval forecasting of electricity demand: A novel bivariate EMD-based support vector regression modeling framework","volume":"63","author":"Xiong","year":"2014","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/j.eneco.2015.02.018","article-title":"A novel hybrid method for crude oil price forecasting","volume":"49","author":"Zhang","year":"2015","journal-title":"Energy Econ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.neucom.2016.03.054","article-title":"A new intelligent method based on combination of VMD and ELM for short term wind power forecasting","volume":"203","author":"Abdoos","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.enconman.2015.02.023","article-title":"Simultaneous day-ahead forecasting of electricity price and load in smart grids","volume":"95","author":"Shayeghi","year":"2015","journal-title":"Energy Convers. Manag."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.atmosenv.2016.03.056","article-title":"A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting","volume":"134","author":"Niu","year":"2016","journal-title":"Atmos. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"10631","DOI":"10.1016\/j.apm.2016.08.001","article-title":"A hybrid forecasting approach applied in the electrical power system based on data preprocessing, optimization and artificial intelligence algorithms","volume":"40","author":"Jiang","year":"2016","journal-title":"Appl. Math. Model."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1098\/rspa.2006.1761","article-title":"Intrinsic time-scale decomposition: Time-frequency-energy analysis and real-time filtering of non-stationary signals","volume":"463","author":"Frei","year":"2007","journal-title":"Proc. R. Soc. A"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.enconman.2015.04.057","article-title":"Four wind speed multi-step forecasting models using extreme learning machines and signal decomposing algorithms","volume":"100","author":"Liu","year":"2015","journal-title":"Energy Convers. Manag."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. Lond. A"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.192463","article-title":"A theory for multiresolution signal decomposition: The wavelet representation","volume":"11","author":"Mallat","year":"1989","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","article-title":"Variational mode decomposition","volume":"62","author":"Dragomiretskiy","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.eswa.2016.02.025","article-title":"A variational mode decompoisition approach for analysis and forecasting of economic and financial time series","volume":"55","author":"Lahmiri","year":"2016","journal-title":"Expert Syst. Appl."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/10\/3\/108\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:44:45Z","timestamp":1760208285000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/10\/3\/108"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9,12]]},"references-count":52,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2017,9]]}},"alternative-id":["a10030108"],"URL":"https:\/\/doi.org\/10.3390\/a10030108","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,9,12]]}}}