{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T20:58:18Z","timestamp":1782680298061,"version":"3.54.5"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T00:00:00Z","timestamp":1656892800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T00:00:00Z","timestamp":1656892800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001824","name":"Grantov\u00e1 Agentura \u010cesk\u00e9 Republiky","doi-asserted-by":"publisher","award":["19-15498S"],"award-info":[{"award-number":["19-15498S"]}],"id":[{"id":"10.13039\/501100001824","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1007\/s00521-022-07504-x","type":"journal-article","created":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T11:14:06Z","timestamp":1656933246000},"page":"19423-19439","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Neural intuitionistic fuzzy system with justified granularity"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5579-1215","authenticated-orcid":false,"given":"Petr","family":"Hajek","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wojciech","family":"Froelich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vladimir","family":"Olej","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josef","family":"Novotny","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,7,4]]},"reference":[{"issue":"4","key":"7504_CR1","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.1007\/s11053-019-09473-w","volume":"28","author":"Z Alameer","year":"2019","unstructured":"Alameer Z, Abd Elaziz M, Ewees AA et al (2019) Forecasting copper prices using hybrid adaptive neuro-fuzzy inference system and genetic algorithms. Nat Resour Res 28(4):1385\u20131401","journal-title":"Nat Resour Res"},{"key":"7504_CR2","first-page":"51","volume":"64","author":"P Angelov","year":"1995","unstructured":"Angelov P (1995) Crispification: defuzzification of intuitionistic fuzzy sets. BUSEFAL 64:51\u201355","journal-title":"BUSEFAL"},{"key":"7504_CR3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-7908-1870-3","volume-title":"Intuitionistic fuzzy sets","author":"KT Atanassov","year":"1999","unstructured":"Atanassov KT (1999) Intuitionistic Fuzzy Sets. Physica-Verlag HD, Heidelberg, pp 1\u2013137"},{"key":"7504_CR4","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/j.engappai.2018.04.017","volume":"72","author":"E Bas","year":"2018","unstructured":"Bas E, Grosan C, Egrioglu E et al (2018) High order fuzzy time series method based on pi-sigma neural network. Eng Appl Artif Intell 72:350\u2013356","journal-title":"Eng Appl Artif Intell"},{"key":"7504_CR5","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1016\/j.eswa.2016.07.044","volume":"64","author":"K Bisht","year":"2016","unstructured":"Bisht K, Kumar S (2016) Fuzzy time series forecasting method based on hesitant fuzzy sets. Expert Syst Appl 64:557\u2013568","journal-title":"Expert Syst Appl"},{"issue":"4","key":"7504_CR6","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1007\/s41066-018-00144-4","volume":"4","author":"K Bisht","year":"2019","unstructured":"Bisht K, Kumar S (2019) Hesitant fuzzy set based computational method for financial time series forecasting. Granul Comput 4(4):655\u2013669","journal-title":"Granul Comput"},{"key":"7504_CR7","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.asoc.2017.11.011","volume":"63","author":"M Bose","year":"2018","unstructured":"Bose M, Mali K (2018) A novel data partitioning and rule selection technique for modeling high-order fuzzy time series. Appl Soft Comput 63:87\u201396","journal-title":"Appl Soft Comput"},{"key":"7504_CR8","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.ijar.2019.05.002","volume":"111","author":"M Bose","year":"2019","unstructured":"Bose M, Mali K (2019) Designing fuzzy time series forecasting models: A survey. Int J Approx Reason 111:78\u201399","journal-title":"Int J Approx Reason"},{"issue":"7","key":"7504_CR9","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1007\/s00521-017-3125-2","volume":"29","author":"I Bougoudis","year":"2018","unstructured":"Bougoudis I, Demertzis K, Iliadis L et al (2018) Fussffra, a fuzzy semi-supervised forecasting framework: the case of the air pollution in athens. Neural Comput Appl 29(7):375\u2013388","journal-title":"Neural Comput Appl"},{"issue":"1\u20134","key":"7504_CR10","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/S0020-0255(02)00264-5","volume":"147","author":"G Chen","year":"2002","unstructured":"Chen G, Wei Q (2002) Fuzzy association rules and the extended mining algorithms. Inf Sci 147(1\u20134):201\u2013228","journal-title":"Inf Sci"},{"key":"7504_CR11","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1016\/j.future.2013.09.025","volume":"37","author":"MY Chen","year":"2014","unstructured":"Chen MY (2014) A high-order fuzzy time series forecasting model for internet stock trading. Future Gener Comput Syst 37:461\u2013467","journal-title":"Future Gener Comput Syst"},{"key":"7504_CR12","doi-asserted-by":"publisher","first-page":"1671","DOI":"10.1016\/j.neucom.2015.09.040","volume":"173","author":"W Deng","year":"2016","unstructured":"Deng W, Wang G, Zhang X et al (2016) A multi-granularity combined prediction model based on fuzzy trend forecasting and particle swarm techniques. Neurocomputing 173:1671\u20131682","journal-title":"Neurocomputing"},{"issue":"10","key":"7504_CR13","doi-asserted-by":"publisher","first-page":"5089","DOI":"10.1007\/s00521-020-05276-w","volume":"33","author":"H Ding","year":"2021","unstructured":"Ding H, Li W, Qiao J (2021) A self-organizing recurrent fuzzy neural network based on multivariate time series analysis. Neural Comput Appl 33(10):5089\u20135109","journal-title":"Neural Comput Appl"},{"issue":"101","key":"7504_CR14","first-page":"881","volume":"69","author":"P Du","year":"2020","unstructured":"Du P, Wang J, Yang W et al (2020) Point and interval forecasting for metal prices based on variational mode decomposition and an optimized outlier-robust extreme learning machine. Resour Policy 69(101):881","journal-title":"Resour Policy"},{"issue":"7","key":"7504_CR15","doi-asserted-by":"publisher","first-page":"10,589","DOI":"10.1016\/j.eswa.2009.02.057","volume":"36","author":"E Egrioglu","year":"2009","unstructured":"Egrioglu E, Aladag CH, Yolcu U et al (2009) A new approach based on artificial neural networks for high order multivariate fuzzy time series. Expert Syst Appl 36(7):10,589-10,594","journal-title":"Expert Syst Appl"},{"issue":"5","key":"7504_CR16","doi-asserted-by":"publisher","first-page":"2672","DOI":"10.1109\/TFUZZ.2018.2803751","volume":"26","author":"I Eyoh","year":"2018","unstructured":"Eyoh I, John R, De Maere G et al (2018) Hybrid learning for interval type-2 intuitionistic fuzzy logic systems as applied to identification and prediction problems.IEEE Trans Fuzzy Syst 26(5):2672\u20132685","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"7504_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2013.11.006","volume":"260","author":"F Gaxiola","year":"2014","unstructured":"Gaxiola F, Melin P, Valdez F et al (2014) Interval type-2 fuzzy weight adjustment for backpropagation neural networks with application in time series prediction. Inf Sci 260:1\u201314","journal-title":"Inf Sci"},{"issue":"4","key":"7504_CR18","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1007\/s41066-019-00168-4","volume":"4","author":"KK Gupta","year":"2019","unstructured":"Gupta KK, Kumar S (2019) A novel high-order fuzzy time series forecasting method based on probabilistic fuzzy sets. Granul Comput 4(4):699\u2013713","journal-title":"Granul Comput"},{"key":"7504_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116487","volume":"193","author":"P Hajek","year":"2022","unstructured":"Hajek P, Novotny J (2022) Fuzzy rule-based prediction of gold prices using news affect. Expert Syst Appl 193:116487","journal-title":"Expert Syst Appl"},{"issue":"1","key":"7504_CR20","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/s12530-016-9157-5","volume":"8","author":"P Hajek","year":"2017","unstructured":"Hajek P, Olej V (2017) Intuitionistic neuro-fuzzy network with evolutionary adaptation. Evol Syst 8(1):35\u201347","journal-title":"Evolv Syst"},{"key":"7504_CR21","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.neucom.2020.03.013","volume":"400","author":"P Hajek","year":"2020","unstructured":"Hajek P, Froelich W, Prochazka O (2020) Intuitionistic fuzzy grey cognitive maps for forecasting interval-valued time series. Neurocomputing 400:173\u2013185","journal-title":"Neurocomputing"},{"key":"7504_CR22","doi-asserted-by":"crossref","unstructured":"Hajek P, Olej V, Froelich W, et al (2021) Intuitionistic fuzzy neural network for time series forecasting - the case of metal prices. In: IFIP International Conference on Artificial Intelligence Applications and Innovations, Springer, pp 411\u2013422","DOI":"10.1007\/978-3-030-79150-6_33"},{"issue":"39","key":"7504_CR23","doi-asserted-by":"publisher","first-page":"4141","DOI":"10.1080\/00036846.2015.1026580","volume":"47","author":"H Hassani","year":"2015","unstructured":"Hassani H, Silva ES, Gupta R et al (2015) Forecasting the price of gold. Appl Econ 47(39):4141\u20134152","journal-title":"Appl Econ"},{"key":"7504_CR24","volume-title":"Forecasting: principles and practice","author":"R Hyndman","year":"2018","unstructured":"Hyndman R, Athanasopoulos G (2018) Forecasting: Principles and Practice. OTexts"},{"key":"7504_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-021-04187-w","author":"SB Jabeur","year":"2021","unstructured":"Jabeur SB, Mefteh-Wali S, Viviani JL (2021) Forecasting gold price with the XGBoost algorithm and SHAP interaction values. Ann Oper Res. https:\/\/doi.org\/10.1007\/s10479-021-04187-w","journal-title":"Ann Oper Res"},{"key":"7504_CR26","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.eswa.2017.05.024","volume":"84","author":"W Kristjanpoller","year":"2017","unstructured":"Kristjanpoller W, Hern\u00e1ndez E (2017) Volatility of main metals forecasted by a hybrid ANN-GARCH model with regressors. Expert Syst Appl 84:290\u2013300","journal-title":"Expert Syst Appl"},{"issue":"5","key":"7504_CR27","doi-asserted-by":"publisher","first-page":"1403","DOI":"10.1007\/s40815-019-00652-8","volume":"21","author":"S Kumar","year":"2019","unstructured":"Kumar S et al (2019) A modified weighted fuzzy time series model for forecasting based on two-factors logical relationship. Int J Fuzzy Syst 21(5):1403\u20131417","journal-title":"Int J Fuzzy Syst"},{"key":"7504_CR28","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.resourpol.2015.03.004","volume":"45","author":"FS Lasheras","year":"2015","unstructured":"Lasheras FS, de Cos Juez FJ, S\u00e1nchez AS et al (2015) Forecasting the COMEX copper spot price by means of neural networks and ARIMA models. Resour Policy 45:37\u201343","journal-title":"Resour Policy"},{"key":"7504_CR29","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/j.ins.2019.08.058","volume":"508","author":"F Li","year":"2020","unstructured":"Li F, Yu F (2020) Multi-factor one-order cross-association fuzzy logical relationships based forecasting models of time series. Inform Sci 508:309\u2013328","journal-title":"Inform Sci"},{"issue":"8","key":"7504_CR30","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.1109\/TFUZZ.2019.2922152","volume":"28","author":"PC de Lima Silva","year":"2019","unstructured":"de Lima Silva PC, Sadaei HJ, Ballini R et al (2019) Probabilistic forecasting with fuzzy time series. IEEE Trans Fuzzy Syst 28(8):1771\u20131784","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"7504_CR31","doi-asserted-by":"crossref","unstructured":"Liu D, Li Z (2017) Gold price forecasting and related influence factors analysis based on random forest. In: Proceedings of the tenth international conference on management science and engineering management, Springer, pp 711\u2013723","DOI":"10.1007\/978-981-10-1837-4_59"},{"key":"7504_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105006","volume":"188","author":"Y Liu","year":"2020","unstructured":"Liu Y, Yang C, Huang K et al (2020) Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network. Knowl-Based Syst 188:105006","journal-title":"Knowl-Based Syst"},{"issue":"23","key":"7504_CR33","doi-asserted-by":"publisher","first-page":"17351","DOI":"10.1007\/s00521-020-04867-x","volume":"32","author":"IE Livieris","year":"2020","unstructured":"Livieris IE, Pintelas E, Pintelas P (2020) A CNN-LSTM model for gold price time-series forecasting. Neural Comput Appl 32(23):17351\u201317360","journal-title":"Neural Comput Appl"},{"issue":"8","key":"7504_CR34","doi-asserted-by":"publisher","first-page":"3799","DOI":"10.1016\/j.eswa.2013.12.005","volume":"41","author":"W Lu","year":"2014","unstructured":"Lu W, Pedrycz W, Liu X et al (2014) The modeling of time series based on fuzzy information granules. Expert Syst Appl 41(8):3799\u20133808","journal-title":"Expert Syst Appl"},{"key":"7504_CR35","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.asoc.2019.02.032","volume":"78","author":"C Luo","year":"2019","unstructured":"Luo C, Tan C, Wang X et al (2019) An evolving recurrent interval type-2 intuitionistic fuzzy neural network for online learning and time series prediction. Appl Soft Comput 78:150\u2013163","journal-title":"Appl Soft Comput"},{"key":"7504_CR36","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06263-5","author":"L Maciel","year":"2021","unstructured":"Maciel L, Ballini R, Gomide F (2021) Adaptive fuzzy modeling of interval-valued stream data and application in cryptocurrencies prediction. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-021-06263-5","journal-title":"Neural Comput Appl"},{"issue":"9","key":"7504_CR37","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1002\/fut.21685","volume":"35","author":"PK Narayan","year":"2015","unstructured":"Narayan PK, Ahmed HA, Narayan S (2015) Do momentum-based trading strategies work in the commodity futures markets? J Futures Mark 35(9):868\u2013891","journal-title":"J Futures Mark"},{"issue":"10","key":"7504_CR38","doi-asserted-by":"publisher","first-page":"4209","DOI":"10.1016\/j.asoc.2013.06.017","volume":"13","author":"W Pedrycz","year":"2013","unstructured":"Pedrycz W, Homenda W (2013) Building the fundamentals of granular computing: a principle of justifiable granularity. Appl Soft Comput 13(10):4209\u20134218","journal-title":"Appl Soft Comput"},{"key":"7504_CR39","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1109\/3477.907568","volume":"31","author":"W Pedrycz","year":"2001","unstructured":"Pedrycz W, Vukovich G (2001) Abstraction and specialization of information granules. IEEE Trans Syst Man Cybern Part B Cybern Publ IEEE Syst Man Cybern Soc 31:106\u201311","journal-title":"IEEE Trans Syst Man Cybern Part B Cybern Publ IEEE Syst Man Cybern Soc"},{"issue":"12","key":"7504_CR40","doi-asserted-by":"publisher","first-page":"2397","DOI":"10.1007\/s00500-013-1213-5","volume":"18","author":"W Pedrycz","year":"2014","unstructured":"Pedrycz W, Lu W, Liu X et al (2014) Human-centric analysis and interpretation of time series: a perspective of granular computing. Soft Comput 18(12):2397\u20132411","journal-title":"Soft Comput"},{"key":"7504_CR41","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1016\/j.asoc.2015.03.059","volume":"32","author":"HW Peng","year":"2015","unstructured":"Peng HW, Wu SF, Wei CC et al (2015) Time series forecasting with a neuro-fuzzy modeling scheme. Appl Soft Comput 32:481\u2013493","journal-title":"Appl Soft Comput"},{"issue":"5","key":"7504_CR42","doi-asserted-by":"publisher","first-page":"1991","DOI":"10.1007\/s00500-015-1619-3","volume":"20","author":"A Roy","year":"2016","unstructured":"Roy A (2016) A novel multivariate fuzzy time series based forecasting algorithm incorporating the effect of clustering on prediction. Soft Comput 20(5):1991\u20132019","journal-title":"Soft Comput"},{"key":"7504_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.resourpol.2019.101542","volume":"65","author":"AA Salisu","year":"2020","unstructured":"Salisu AA, Ogbonna AE, Adewuyi A (2020) Google trends and the predictability of precious metals. Resour Policy 65:101542","journal-title":"Resour Policy"},{"key":"7504_CR44","volume-title":"Practical time series forecasting","author":"G Shmueli","year":"2011","unstructured":"Shmueli G (2011) Practical time series forecasting, 2nd edn. Statistics.com LLC, Arlington","edition":"2"},{"issue":"12","key":"7504_CR45","doi-asserted-by":"publisher","first-page":"3851","DOI":"10.1007\/s00521-016-2261-4","volume":"28","author":"P Singh","year":"2017","unstructured":"Singh P (2017) High-order fuzzy-neuro-entropy integration-based expert system for time series forecasting. Neural Comput Appl 28(12):3851\u20133868","journal-title":"Neural Comput Appl"},{"key":"7504_CR46","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.knosys.2013.01.030","volume":"46","author":"P Singh","year":"2013","unstructured":"Singh P, Borah B (2013) High-order fuzzy-neuro expert system for time series forecasting. Knowl-Based Syst 46:12\u201321","journal-title":"Knowl-Based Syst"},{"key":"7504_CR47","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1016\/j.jocs.2018.05.008","volume":"27","author":"P Singh","year":"2018","unstructured":"Singh P, Dhiman G (2018) A hybrid fuzzy time series forecasting model based on granular computing and bio-inspired optimization approaches. J Comput Sci 27:370\u2013385","journal-title":"J Comput Sci"},{"issue":"3","key":"7504_CR48","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/0165-0114(93)90372-O","volume":"54","author":"Q Song","year":"1993","unstructured":"Song Q, Chissom BS (1993) Fuzzy time series and its models. Fuzzy Sets Syst 54(3):269\u2013277","journal-title":"Fuzzy Sets Syst"},{"issue":"3","key":"7504_CR49","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1007\/s40815-017-0443-6","volume":"20","author":"J Soto","year":"2018","unstructured":"Soto J, Melin P, Castillo O (2018) A new approach for time series prediction using ensembles of it2fnn models with optimization of fuzzy integrators. Int J Fuzzy Syst 20(3):701\u2013728","journal-title":"Int J Fuzzy Syst"},{"key":"7504_CR50","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1016\/j.neucom.2016.03.068","volume":"205","author":"CH Su","year":"2016","unstructured":"Su CH, Cheng CH (2016) A hybrid fuzzy time series model based on ANFIS and integrated nonlinear feature selection method for forecasting stock. Neurocomputing 205:264\u2013273","journal-title":"Neurocomputing"},{"key":"7504_CR51","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.ijar.2015.12.011","volume":"70","author":"FM Talarposhti","year":"2016","unstructured":"Talarposhti FM, Sadaei HJ, Enayatifar R et al (2016) Stock market forecasting by using a hybrid model of exponential fuzzy time series. Int J Approx Reason 70:79\u201398","journal-title":"Int J Approx Reason"},{"key":"7504_CR52","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2021.3062723","author":"Y Tang","year":"2021","unstructured":"Tang Y, Yu F, Pedrycz W et al (2021) Building trend fuzzy granulation based LSTM recurrent neural network for long-term time series forecasting. IEEE Trans Fuzzy Syst. https:\/\/doi.org\/10.1109\/TFUZZ.2021.3062723","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"101","key":"7504_CR53","first-page":"414","volume":"63","author":"C Wang","year":"2019","unstructured":"Wang C, Zhang X, Wang M et al (2019) Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques. Resour Policy 63(101):414","journal-title":"Resour Policy"},{"issue":"6","key":"7504_CR54","doi-asserted-by":"publisher","first-page":"3134","DOI":"10.1016\/j.eswa.2013.10.046","volume":"41","author":"L Wang","year":"2014","unstructured":"Wang L, Liu X, Pedrycz W et al (2014) Determination of temporal information granules to improve forecasting in fuzzy time series. Expert Syst Appl 41(6):3134\u20133142","journal-title":"Expert Syst Appl"},{"issue":"4","key":"7504_CR55","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1109\/TFUZZ.2008.924329","volume":"17","author":"D Wu","year":"2009","unstructured":"Wu D, Mendel JM (2009) Enhanced Karnik\u2013Mendel algorithms. IEEE Trans Fuzzy Syst 17(4):923\u2013934","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"4","key":"7504_CR56","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1002\/for.2734","volume":"40","author":"H Wu","year":"2021","unstructured":"Wu H, Long H, Wang Y et al (2021) Stock index forecasting: a new fuzzy time series forecasting method. J Forecast 40(4):653\u2013666","journal-title":"J Forecast"},{"key":"7504_CR57","first-page":"94","volume-title":"Computational intelligence: soft computing and fuzzy-neuro integration with applications","author":"RR Yager","year":"1998","unstructured":"Yager RR (1998) Measures of specificity. In: Kaynak O, Zadeh LA, Turksen B, Rudas IM (eds) Computational intelligence: soft computing and fuzzy-neuro integration with applications. Springer, Berlin, pp 94\u2013113"},{"key":"7504_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ijar.2016.10.010","volume":"81","author":"X Yang","year":"2017","unstructured":"Yang X, Yu F, Pedrycz W (2017) Long-term forecasting of time series based on linear fuzzy information granules and fuzzy inference system. Int J Approx Reason 81:1\u201327","journal-title":"Int J Approx Reason"},{"issue":"19\u201320","key":"7504_CR59","doi-asserted-by":"publisher","first-page":"8750","DOI":"10.1016\/j.apm.2016.05.012","volume":"40","author":"OC Yolcu","year":"2016","unstructured":"Yolcu OC, Yolcu U, Egrioglu E et al (2016) High order fuzzy time series forecasting method based on an intersection operation. Appl Math Model 40(19\u201320):8750\u20138765","journal-title":"Appl Math Model"},{"issue":"4","key":"7504_CR60","doi-asserted-by":"publisher","first-page":"3366","DOI":"10.1016\/j.eswa.2009.10.013","volume":"37","author":"THK Yu","year":"2010","unstructured":"Yu THK, Huarng KH (2010) A neural network-based fuzzy time series model to improve forecasting. Expert Syst Appl 37(4):3366\u20133372","journal-title":"Expert Syst Appl"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07504-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07504-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07504-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T04:33:18Z","timestamp":1668486798000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07504-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,4]]},"references-count":60,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["7504"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07504-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,4]]},"assertion":[{"value":"26 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 July 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}