{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:19:13Z","timestamp":1777706353602,"version":"3.51.4"},"reference-count":41,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2022,5,22]],"date-time":"2022-05-22T00:00:00Z","timestamp":1653177600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems: Applications in Engineering and Technology"],"published-print":{"date-parts":[[2022,8,10]]},"abstract":"<jats:p>\n                    Asymmetric\n                    <jats:italic toggle=\"yes\">\u03bd<\/jats:italic>\n                    -twin Support vector regression (Asy-\n                    <jats:italic toggle=\"yes\">\u03bd<\/jats:italic>\n                    -TSVR) is an effective regression model in price prediction. However, there is a matrix inverse operation when solving its dual problem. It is well known that it may be not reversible, therefore a regularized asymmetric\n                    <jats:italic toggle=\"yes\">\u03bd<\/jats:italic>\n                    -TSVR (RAsy-\n                    <jats:italic toggle=\"yes\">\u03bd<\/jats:italic>\n                    -TSVR) is proposed in this paper to avoid above problem. Numerical experiments on eight Benchmark datasets are conducted to demonstrate the validity of our proposed RAsy-\n                    <jats:italic toggle=\"yes\">\u03bd<\/jats:italic>\n                    -TSVR. Moreover, a statistical test is to further show the effectiveness. Before we apply it to Chinese soybean price forecasting, we firstly employ the Lasso to analyze the influence factors of soybean price, and select 21 important factors from the original 25 factors. And then RAsy-\n                    <jats:italic toggle=\"yes\">\u03bd<\/jats:italic>\n                    -TSVR is used to forecast the Chinese soybean price. It yields the lowest prediction error compared with other four models in both the training and testing phases. Meanwhile it produces lower prediction error after the feature selection than before. So the combined Lasso and RAsy-\n                    <jats:italic toggle=\"yes\">\u03bd<\/jats:italic>\n                    -TSVR model is effective for the Chinese soybean price.\n                  <\/jats:p>","DOI":"10.3233\/jifs-212525","type":"journal-article","created":{"date-parts":[[2022,5,24]],"date-time":"2022-05-24T11:51:52Z","timestamp":1653393112000},"page":"4859-4872","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Soybean price forecasting based on Lasso and regularized asymmetric\n                    <i>\u03bd<\/i>\n                    -TSVR"],"prefix":"10.1177","volume":"43","author":[{"given":"Chang","family":"Xu","sequence":"first","affiliation":[{"name":"China Agricultural University","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[{"name":"China Agricultural University","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingxian","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Agricultural University","place":["China"]},{"name":"Key Laboratory of Agricultural Informationization Standardization, Ministry of Agriculture and Rural Affairs, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2022,5,22]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1024146710611"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2018.08.016"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.06.071"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1088\/1755-1315\/94\/1\/012121"},{"key":"e_1_3_3_6_2","first-page":"3666","article-title":"Advances in research of short-termforecasting methods of agricultural product price","volume":"44","author":"Xu S.","year":"2011","unstructured":"XuS., LiZ., LiG., et al., Advances in research of short-termforecasting methods of agricultural product price, Sci AgricSin 44 (2011), 3666\u20133675.","journal-title":"Sci AgricSin"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.08.027"},{"issue":"3","key":"e_1_3_3_8_2","first-page":"838","article-title":"Forecasting food prices: The case ofcorn, soybeans and wheat","volume":"32","author":"Ahumada H.","year":"2016","unstructured":"AhumadaH., CornejoM., et al., Forecasting food prices: The case ofcorn, soybeans and wheat, Journal of Forecasting 32(3) (2016), 838\u2013848.","journal-title":"Journal of Forecasting"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1093\/erae\/jbz009"},{"key":"e_1_3_3_10_2","first-page":"1","article-title":"An EmpiricalAnalysis of the Price Volatility Characteristics of China\u0105r'sSoybean Futures Market Based on ARIMA-GJR-GARCH Model","volume":"2021","author":"Xu Y.","year":"2021","unstructured":"XuY., XiaZ., WangC., GongW., LiuX., SuX., et al., An EmpiricalAnalysis of the Price Volatility Characteristics of China\u0105r'sSoybean Futures Market Based on ARIMA-GJR-GARCH Model, Journalof Mathematics 2021 (2021), 1\u20139.","journal-title":"Journalof Mathematics"},{"issue":"2020","key":"e_1_3_3_11_2","first-page":"105837","article-title":"Ensemble approachbased on bagging, boosting and stacking for short-term prediction inagribusiness time series","volume":"86","author":"Ribeiro M.H.D.M.","unstructured":"RibeiroM.H.D.M., Dos Santos CoelhoL., et al., Ensemble approachbased on bagging, boosting and stacking for short-term prediction inagribusiness time series, Applied Soft Computing 86(2020), 105837.","journal-title":"Applied Soft Computing"},{"issue":"4","key":"e_1_3_3_12_2","first-page":"4477","article-title":"A novel recurrent neural networkalgorithm with long short-term memory model for futures trading","volume":"37","author":"Gu Q.","year":"2019","unstructured":"GuQ., LuN., LiuL., et al., A novel recurrent neural networkalgorithm with long short-term memory model for futures trading, Journal of Intelligent 37(4) (2019), 4477\u20134484.","journal-title":"Journal of Intelligent"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-01814-0"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1134\/S0006350915010145"},{"key":"e_1_3_3_15_2","first-page":"1658","article-title":"Support vector regression withmodified firefly algorithm for stock price forecasting","volume":"49","author":"Zhang J.","year":"2019","unstructured":"ZhangJ., TengY. and ChenW., Support vector regression withmodified firefly algorithm for stock price forecasting, ApplIntell 49 (2019), 1658\u20131674.","journal-title":"ApplIntell"},{"key":"e_1_3_3_16_2","first-page":"675","article-title":"A Study on the Comparison of ElectricityForecasting Models: Korea and China","volume":"22","author":"Zheng X.","year":"2015","unstructured":"ZhengX. and KimS., A Study on the Comparison of ElectricityForecasting Models: Korea and China, Commun Stat Appl Methods 22 (2015), 675\u2013683.","journal-title":"Commun Stat Appl Methods"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.06.016"},{"issue":"2019","key":"e_1_3_3_18_2","first-page":"228","article-title":"Global stock marketinvestment strategies based on financial network indicators usingmachine learning techniques","volume":"117","author":"Lee T.K.","unstructured":"LeeT.K., ChoJ.H., KwonD.S. et al., Global stock marketinvestment strategies based on financial network indicators usingmachine learning techniques, Expert Syst Appl 117(2019), 228\u2013242.","journal-title":"Expert Syst Appl"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113799"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-019-01465-w"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300015565"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.77"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2012.03.013"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.119969"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10614-019-09896-w"},{"issue":"2014","key":"e_1_3_3_26_2","first-page":"371","article-title":"Asymmetric \u03bd-tubesupport vector regression","volume":"77","author":"Huang X.","unstructured":"HuangX., ShiL., PelckmansK. et al., Asymmetric \u03bd-tubesupport vector regression, Comput Stat Data Anal 77(2014), 371\u2013382.","journal-title":"Comput Stat Data Anal"},{"key":"e_1_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.1108\/BFJ-09-2019-0683"},{"key":"e_1_3_3_28_2","doi-asserted-by":"publisher","DOI":"10.1002\/agr.20292"},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-017-2966-z"},{"key":"e_1_3_3_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2009.07.002"},{"key":"e_1_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2015.10.007"},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-013-0500-2"},{"key":"e_1_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(03)00169-2"},{"key":"e_1_3_3_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1068"},{"key":"e_1_3_3_35_2","first-page":"1","article-title":"Statistical comparisons of classifiers over multipledata sets","volume":"7","author":"Dem\u0161ar J.","year":"2006","unstructured":"Dem\u0161arJ., Statistical comparisons of classifiers over multipledata sets, J Mach Learn Res 7 (2006), 1\u201330.","journal-title":"J Mach Learn Res"},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107297"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11071-020-06111-6"},{"key":"e_1_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2011.00771.x"},{"issue":"2010","key":"e_1_3_3_39_2","first-page":"204","article-title":"The sufficiency of target costing for evaluatingproduction-related decisions","volume":"126","author":"Kee R.","unstructured":"KeeR., The sufficiency of target costing for evaluatingproduction-related decisions, Int J Prod Econ 126(2010), 204\u2013211.","journal-title":"Int J Prod Econ"},{"issue":"2018","key":"e_1_3_3_40_2","first-page":"184","article-title":"Agricultural value chain: Concepts, definitions and analysis tool","volume":"11","author":"Pal D.","unstructured":"PalD. and SharmaL., Agricultural value chain: Concepts, definitions and analysis tool, Int J Commer Bus Manag 11(2018), 184\u2013190.","journal-title":"Int J Commer Bus Manag"},{"issue":"2021","key":"e_1_3_3_41_2","first-page":"15","article-title":"An attribution analysis of soybeanprice volatility in China: global market connectedness or energymarket transmission?","volume":"24","author":"Zhang Y.","unstructured":"ZhangY., LiC., XuY., et al., An attribution analysis of soybeanprice volatility in China: global market connectedness or energymarket transmission? Int Food Agribus Manag Rev 24(2021), 15\u201325.","journal-title":"Int Food Agribus Manag Rev"},{"key":"e_1_3_3_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.agsy.2020.102850"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems: Applications in Engineering and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-212525","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-212525","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-212525","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:46:39Z","timestamp":1777455999000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-212525"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,22]]},"references-count":41,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,8,10]]}},"alternative-id":["10.3233\/JIFS-212525"],"URL":"https:\/\/doi.org\/10.3233\/jifs-212525","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,22]]}}}