{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:35:52Z","timestamp":1777703752052,"version":"3.51.4"},"reference-count":35,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2018,7,4]],"date-time":"2018-07-04T00:00:00Z","timestamp":1530662400000},"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"],"published-print":{"date-parts":[[2018,7,27]]},"abstract":"<jats:p>We propose a heuristic learning method forecasting future performance of stock market indices based on high-order fuzzy-trend jump rules generated from historical training data. Firstly, the training time series (TSs) are fuzzified by equal intervals referencing to the whole mean differences of historical training data. Then, it generates the groups of nth-order fuzzy logical relationships (FLRs). With the knowledge of the generated relationship groups, it summarizes the probability of the jumps of the nth-order \u201cdown\u201d, \u201cequal\u201d and \u201cup\u201d trend rules, respectively. Finally, it performs the forecasting based on the nth-order FLRs and the probabilities of their corresponding jump rules. To evaluate the outcome of the presented model with the performances of the others, we use the presented model to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) dataset. The outcomes show that the presented model outperforms the other models using single factor and point-wise one-step ahead forecasts. Moreover, it is easily to realize by software computing without artificial participation and can be extended to deal with multiple years of dataset. We use this model to predict Shanghai Stock Exchange Composite Index (SHSECI) as well to analyze its effectiveness and universality.<\/jats:p>","DOI":"10.3233\/jifs-169585","type":"journal-article","created":{"date-parts":[[2018,7,6]],"date-time":"2018-07-06T11:59:25Z","timestamp":1530878365000},"page":"257-267","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Forecasting model based on heuristic learning of high-order fuzzy-trend and jump rules"],"prefix":"10.1177","volume":"35","author":[{"given":"Hongjun","family":"Guan","sequence":"first","affiliation":[{"name":"School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, China"}]},{"given":"Zongli","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, China"}]},{"given":"Shuang","family":"Guan","sequence":"additional","affiliation":[{"name":"Rensselaer Polytechnic Institute, Troy, New York, USA"}]},{"given":"Aiwu","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Management, Jiangsu University, Zhenjiang, China"}]}],"member":"179","published-online":{"date-parts":[[2018,7,4]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-169161"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.econmod.2012.09.047"},{"issue":"2","key":"e_1_3_2_4_2","first-page":"847","article-title":"Modified support vector machines infinancial time series forecasting","volume":"48","author":"HTay F.E.","year":"2002","unstructured":"HTayF.E. and CaoL.J., Modified support vector machines infinancial time series forecasting, Neurocomputing48(2) (2002), 847\u2013861.","journal-title":"Neurocomputing"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2005.07.004"},{"issue":"3","key":"e_1_3_2_6_2","doi-asserted-by":"crossref","first-page":"6108","DOI":"10.1016\/j.eswa.2008.07.043","article-title":"An improved method for forecasting enrollments basedon fuzzy time series and particle swarm optimization","volume":"36","author":"Kuo H.","year":"2009","unstructured":"KuoH., An improved method for forecasting enrollments basedon fuzzy time series and particle swarm optimization, Expert Syst Appl36(3) (2009), 6108\u20136117.","journal-title":"Expert Syst Appl"},{"issue":"3","key":"e_1_3_2_7_2","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1016\/j.physa.2004.11.006","article-title":"Weighted fuzzy time-series model for TAIEX forecasting","volume":"349","author":"Yu H.K.","year":"2005","unstructured":"YuH.K., Weighted fuzzy time-series model for TAIEX forecasting, Phys A,(3\u20134) 349 (2005), 609\u2013624.","journal-title":"Phys A"},{"key":"e_1_3_2_8_2","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/j.asoc.2015.03.059","article-title":"Time series forecastingwith a neuro-fuzzy modeling scheme","volume":"32","author":"Peng H.W.","year":"2015","unstructured":"PengH.W., WuS.F., WeiC.C. and LeeS.J., Time series forecastingwith a neuro-fuzzy modeling scheme, Applied Soft Computting32 (2015), 481\u2013493.","journal-title":"Applied Soft Computting"},{"key":"e_1_3_2_9_2","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.eswa.2017.01.049","article-title":"Improving stock index forecastsby using a new weighted fuzzy-trend time series method","volume":"76","author":"Rubio J.D.","year":"2017","unstructured":"RubioJ.D.Bermudez and VercherE., Improving stock index forecastsby using a new weighted fuzzy-trend time series method, ExpertSystems With Applications76 (2017), 12\u201320.","journal-title":"ExpertSystems With Applications"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2010.04.010"},{"key":"e_1_3_2_11_2","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/S0019-9958(65)90241-X","article-title":"Fuzzy sets","volume":"83","author":"Zadeh L.A.","year":"1965","unstructured":"ZadehL.A., Fuzzy sets, Inf Control83 (1965), 338\u2013353.","journal-title":"Inf Control"},{"issue":"3","key":"e_1_3_2_12_2","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1109\/TFUZZ.2006.876367","article-title":"Handling forecasting problems based on two-factors high-order fuzzy time series","volume":"14","author":"Lee L.W.","year":"2006","unstructured":"LeeL.W., WangL.H., ChenS.M. and LeuY.H., Handling forecasting problems based on two-factors high-order fuzzy time series, IEEE Trans Fuzzy Syst14(3) (2006), 468\u2013477.","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"2","key":"e_1_3_2_13_2","first-page":"911","article-title":"A GA-weighted ANFIS model based on multiple stock marketvolatility causality for TAIEX forecasting","volume":"13","author":"Wei L.Y.","year":"2013","unstructured":"WeiL.Y., A GA-weighted ANFIS model based on multiple stock marketvolatility causality for TAIEX forecasting, Applied SoftComputing13(2) (2013), 911\u2013920.","journal-title":"Applied SoftComputing"},{"key":"e_1_3_2_14_2","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.ins.2014.09.038","article-title":"A hybrid fuzzy time series model based ongranular computing for stock price forecasting","volume":"294","author":"Chen M.Y.","year":"2015","unstructured":"ChenM.Y. and ChenB.T., A hybrid fuzzy time series model based ongranular computing for stock price forecasting, Information Science294 (2015), 227\u2013241.","journal-title":"Information Science"},{"issue":"2","key":"e_1_3_2_15_2","doi-asserted-by":"crossref","first-page":"2143","DOI":"10.1016\/j.eswa.2007.12.013","article-title":"Temperature prediction and TAIFEXforecasting based on automatic clustering techniques and two-factorshigh-order fuzzy time series","volume":"36","author":"Wang N.Y.","year":"2009","unstructured":"WangN.Y. and ChenS.M., Temperature prediction and TAIFEXforecasting based on automatic clustering techniques and two-factorshigh-order fuzzy time series, Expert Syst Appl36(2) (2009), 2143\u20132154.","journal-title":"Expert Syst Appl"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.04.047"},{"issue":"6","key":"e_1_3_2_17_2","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.omega.2004.07.024","article-title":"A hybrid ARIMA and support vector machinesmodel in stock price forecasting","volume":"33","author":"Pai P.F.","year":"2005","unstructured":"PaiP.F. and LinC.S., A hybrid ARIMA and support vector machinesmodel in stock price forecasting, Omega33(6) (2005), 497\u2013505.","journal-title":"Omega"},{"issue":"3","key":"e_1_3_2_18_2","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/0165-0114(93)90372-O","article-title":"Fuzzy time series and its models","volume":"54","author":"Song Q.","year":"1993","unstructured":"SongQ. and ChissomB.S., Fuzzy time series and its models, Fuzzy Sets Syst54(3) (1993), 269\u2013277.","journal-title":"Fuzzy Sets Syst"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0114(93)90355-L"},{"issue":"1","key":"e_1_3_2_20_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0165-0114(94)90067-1","article-title":"Forecasting enrollments with fuzzy timeseries\u2014Part II","volume":"62","author":"Song Q.","year":"1994","unstructured":"SongQ. and ChissomB.S., Forecasting enrollments with fuzzy timeseries\u2014Part II, Fuzzy Sets Syst62(1) (1994), 1\u20138.","journal-title":"Fuzzy Sets Syst"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2014.11.043"},{"key":"e_1_3_2_22_2","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.jocs.2015.11.011","article-title":"Intraday stock prime forecasting based on variationalmode decomposition","volume":"12","author":"Lahrimi S.","year":"2016","unstructured":"LahrimiS., Intraday stock prime forecasting based on variationalmode decomposition, Journal of Computational Science12 (2016), 23\u201327.","journal-title":"Journal of Computational Science"},{"issue":"3","key":"e_1_3_2_23_2","first-page":"405","article-title":"Fuzzy forecasting based on two-factorssecond-order fuzzy-trend logical relationship groups and theprobabilities of trends of fuzzy logical relationships","volume":"45","author":"Chen S.M.","year":"2015","unstructured":"ChenS.M. and ChenS.W., Fuzzy forecasting based on two-factorssecond-order fuzzy-trend logical relationship groups and theprobabilities of trends of fuzzy logical relationships, IEEE Transaction on Cybernetics45(3) (2015), 405\u2013417.","journal-title":"IEEE Transaction on Cybernetics"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0114(95)00220-0"},{"issue":"3","key":"e_1_3_2_25_2","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.1109\/TSMCB.2012.2223815","article-title":"Fuzzy forecastingbased on two-factors second-order fuzzy-trend logical relationshipgroups and particle swarm optimization techniques","volume":"43","author":"Chen S.M.","year":"2013","unstructured":"ChenS.M., ManaluG.M.T., PanJ.S. and LiuH.C., Fuzzy forecastingbased on two-factors second-order fuzzy-trend logical relationshipgroups and particle swarm optimization techniques, IEEE Trans Cybern43(3) (2013), 1102\u20131117.","journal-title":"IEEE Trans Cybern"},{"issue":"7","key":"e_1_3_2_26_2","doi-asserted-by":"crossref","first-page":"10551","DOI":"10.1016\/j.eswa.2009.02.061","article-title":"A computational method of forecasting based onhigh-order fuzzy time series","volume":"36","author":"Singh S.R.","year":"2009","unstructured":"SinghS.R., A computational method of forecasting based onhigh-order fuzzy time series, Expert Systems with Applications36(7) (2009), 10551\u201310559.","journal-title":"Expert Systems with Applications"},{"issue":"15","key":"e_1_3_2_27_2","doi-asserted-by":"crossref","first-page":"12158","DOI":"10.1016\/j.eswa.2012.04.039","article-title":"Partitions based computational method forhigh-order fuzzy time series forecasting","volume":"39","author":"Gangwar S.S.","year":"2012","unstructured":"GangwarS.S. and KumarS., Partitions based computational method forhigh-order fuzzy time series forecasting, Expert Systems with Applications39(15) (2012), 12158\u201312164.","journal-title":"Expert Systems with Applications"},{"issue":"5","key":"e_1_3_2_28_2","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1109\/TSMCB.2009.2036860","article-title":"A stochastic HMM-Based forecasting model forfuzzy time series","volume":"40","author":"Li S.T.","year":"2010","unstructured":"LiS.T. and ChengY.C., A stochastic HMM-Based forecasting model forfuzzy time series, IEEE Trans Syst, Man, Cybern B, Cybern40(5) (2010), 1255\u20131266.","journal-title":"IEEE Trans Syst, Man, Cybern B, Cybern"},{"issue":"3","key":"e_1_3_2_29_2","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1080\/01969720802715128","article-title":"An enhanced deterministic fuzzy time seriesforecasting model","volume":"40","author":"Li S.T.","year":"2009","unstructured":"LiS.T. and ChengY.C., An enhanced deterministic fuzzy time seriesforecasting model, Cybern Syst40(3) (2009), 211\u2013235.","journal-title":"Cybern Syst"},{"issue":"12","key":"e_1_3_2_30_2","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1016\/j.camwa.2006.03.036","article-title":"Deterministic fuzzy time series model forforecasting enrollments","volume":"53","author":"Li S.T.","year":"2007","unstructured":"LiS.T. and ChengY.C., Deterministic fuzzy time series model forforecasting enrollments, Comput Math Appl53(12) (2007), 1904\u20131920.","journal-title":"Comput Math Appl"},{"issue":"4","key":"e_1_3_2_31_2","doi-asserted-by":"crossref","first-page":"3366","DOI":"10.1016\/j.eswa.2009.10.013","article-title":"A neural network-based fuzzy time seriesmodel to improve forecasting","volume":"37","author":"Yu T.H.K.","year":"2010","unstructured":"YuT.H.K. and HuarngK.H., A neural network-based fuzzy time seriesmodel to improve forecasting, Expert Syst Appl37(4) (2010), 3366\u20133372.","journal-title":"Expert Syst Appl"},{"issue":"4","key":"e_1_3_2_32_2","doi-asserted-by":"crossref","first-page":"2945","DOI":"10.1016\/j.eswa.2007.05.016","article-title":"A bivariate fuzzy time series model to forecast the TAIEX","volume":"34","author":"Yu T.H.K.","year":"2008","unstructured":"YuT.H.K. and HuarngK.H., A bivariate fuzzy time series model to forecast the TAIEX, Expert Syst Appl34(4) (2008), 2945\u20132952.","journal-title":"Expert Syst Appl"},{"key":"e_1_3_2_33_2","doi-asserted-by":"crossref","first-page":"2510","DOI":"10.1016\/j.asoc.2010.09.007","article-title":"Forecasting stock markets usingwavelet trans-forms and recurrent neural networks: An integratedsystem based on artificial bee colony algorithm","volume":"11","author":"Hsieh T.J.","year":"2011","unstructured":"HsiehT.J., HsiaoH.F. and YehW.C., Forecasting stock markets usingwavelet trans-forms and recurrent neural networks: An integratedsystem based on artificial bee colony algorithm, Applied Soft Computing11 (2011), 2510\u20132525.","journal-title":"Applied Soft Computing"},{"issue":"15","key":"e_1_3_2_34_2","first-page":"570","article-title":"Testing the stability of the US stock market antibubble","volume":"348","author":"Zhou W.X.","year":"2005","unstructured":"ZhouW.X. and SornetteD., Testing the stability of the US stock market antibubble, Physica A: Social Science Electronic Publishing348(15) (2005), 570\u2013578.","journal-title":"Physica A: Social Science Electronic Publishing"},{"issue":"4","key":"e_1_3_2_35_2","doi-asserted-by":"crossref","first-page":"8107","DOI":"10.1016\/j.eswa.2008.10.034","article-title":"A distance-based fuzzy time series model for exchange rates forecasting","volume":"36","author":"Leu Y.","year":"2009","unstructured":"LeuY., LeeC.P. and JouY.Z., A distance-based fuzzy time series model for exchange rates forecasting, Expert Syst Appl36(4) (2009), 8107\u20138114.","journal-title":"Expert Syst Appl"},{"key":"e_1_3_2_36_2","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1007\/s10489-014-0520-6","article-title":"Modeling fitting-function-basedfuzzy time series patterns for evolving stock index forecasting","volume":"41","author":"Chen Y.S.","year":"2014","unstructured":"ChenY.S., ChengC.H. and TsaiW.L., Modeling fitting-function-basedfuzzy time series patterns for evolving stock index forecasting, Applied Intelligence41 (2014), 327\u2013347.","journal-title":"Applied Intelligence"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169585","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-169585","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169585","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:39:28Z","timestamp":1777455568000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-169585"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,4]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,7,27]]}},"alternative-id":["10.3233\/JIFS-169585"],"URL":"https:\/\/doi.org\/10.3233\/jifs-169585","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,4]]}}}