{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T05:02:06Z","timestamp":1781154126353,"version":"3.54.1"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100020884","name":"Agencia Nacional de Investigaci\u00f3n y Desarrollo","doi-asserted-by":"publisher","award":["1240293"],"award-info":[{"award-number":["1240293"]}],"id":[{"id":"10.13039\/501100020884","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018707","name":"HORIZON EUROPE Reforming and enhancing the European Research and Innovation system","doi-asserted-by":"publisher","award":["101120657"],"award-info":[{"award-number":["101120657"]}],"id":[{"id":"10.13039\/100018707","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Evolving Systems"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1007\/s12530-026-09839-5","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T04:50:57Z","timestamp":1781153457000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fast and interpretable time series forecasting using long short-term cognitive networks"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1936-3701","authenticated-orcid":false,"given":"Gonzalo","family":"N\u00e1poles","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8035-2887","authenticated-orcid":false,"given":"Isel","family":"Grau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1946-0053","authenticated-orcid":false,"given":"Yamisleydi","family":"Salgueiro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,11]]},"reference":[{"issue":"10","key":"9839_CR1","doi-asserted-by":"publisher","first-page":"4830","DOI":"10.1016\/j.eswa.2015.01.060","volume":"42","author":"R Al-Hmouz","year":"2015","unstructured":"Al-Hmouz R, Pedrycz W, Balamash A (2015) Description and prediction of time series: a general framework of granular computing. Expert Syst Appl 42(10):4830\u20134839. https:\/\/doi.org\/10.1016\/j.eswa.2015.01.060","journal-title":"Expert Syst Appl"},{"key":"9839_CR2","doi-asserted-by":"publisher","unstructured":"Baer G, Grau I, Zhang C, Van Gorp P (2025) Class-dependent perturbation effects in evaluating time series attributions. In: World conference on explainable artificial intelligence. Springer, p 292\u2013314. https:\/\/doi.org\/10.1007\/978-3-032-08330-2_14.","DOI":"10.1007\/978-3-032-08330-2_14."},{"issue":"5","key":"9839_CR3","first-page":"1","volume":"17","author":"A Benavoli","year":"2016","unstructured":"Benavoli A, Corani G, Mangili F (2016) Should we really use post-hoc tests based on mean-ranks? J Mach Learn Res 17(5):1\u201310","journal-title":"J Mach Learn Res"},{"key":"9839_CR4","doi-asserted-by":"publisher","unstructured":"Challu C, Olivares KG, Oreshkin BN, Ramirez FG, Canseco MM, Dubrawski A (2023) Nhits: neural hierarchical interpolation for time series forecasting. In: Proceedings of the AAAI conference on artificial intelligence, vol 37, p 6989\u20136997. https:\/\/doi.org\/10.1609\/aaai.v37i6.25854","DOI":"10.1609\/aaai.v37i6.25854"},{"key":"9839_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-48963-1","volume-title":"Fuzzy Cognitive maps: best practices and modern methods","author":"PJ Giabbanelli","year":"2024","unstructured":"Giabbanelli PJ, N\u00e1poles G (2024) Fuzzy Cognitive maps: best practices and modern methods. Springer, Switzerland. https:\/\/doi.org\/10.1007\/978-3-031-48963-1"},{"key":"9839_CR6","doi-asserted-by":"publisher","unstructured":"Giabbanelli PJ, Knox CB, Furman K, Jetter A, Gray S (2024) In: Giabbanelli PJ, N\u00e1poles G (eds) Defining and using fuzzy cognitive mapping, Springer, Cham, p 1\u201318. https:\/\/doi.org\/10.1007\/978-3-031-48963-1_1","DOI":"10.1007\/978-3-031-48963-1_1"},{"key":"9839_CR7","unstructured":"Grau I, Hoop M, Glaser A, N\u00e1poles G, Dijkman R (2022) Semiconductor demand forecasting using long short-term cognitive networks. In: 34th Benelux conference on artificial intelligence and 31st Belgian-Dutch conference on machine learning, BNAIC\/BeNeLearn 2022. Antwerpen University"},{"key":"9839_CR8","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. https:\/\/doi.org\/10.1016\/j.neucom.2020.03.013","journal-title":"Neurocomputing"},{"key":"9839_CR9","volume-title":"Neurocomputing","author":"R Hecht-Nielsen","year":"1989","unstructured":"Hecht-Nielsen R (1989) Neurocomputing. AAddison-Wesley Longman Publishing Co., Inc., USA"},{"issue":"8","key":"9839_CR10","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"issue":"7","key":"9839_CR11","doi-asserted-by":"publisher","first-page":"1383","DOI":"10.1109\/TFUZZ.2019.2917126","volume":"28","author":"W Homenda","year":"2020","unstructured":"Homenda W, Jastrzebska A (2020) Time-series classification using fuzzy cognitive maps. IEEE Trans Fuzzy Syst 28(7):1383\u20131394. https:\/\/doi.org\/10.1109\/TFUZZ.2019.2917126","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"2","key":"9839_CR12","doi-asserted-by":"publisher","first-page":"1348","DOI":"10.1109\/TCYB.2021.3133597","volume":"53","author":"A Jastrzebska","year":"2021","unstructured":"Jastrzebska A, N\u00e1poles G, Homenda W, Vanhoof K (2021) Fuzzy cognitive map-driven comprehensive time-series classification. IEEE Trans Cybern 53(2):1348\u20131359. https:\/\/doi.org\/10.1109\/TCYB.2021.3133597","journal-title":"IEEE Trans Cybern"},{"issue":"12","key":"9839_CR13","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-025-06745-2","volume":"55","author":"T Jiacheng","year":"2025","unstructured":"Jiacheng T, Fengqian D, Rui S, Chao L (2025) Spatial-temporal fuzzy cognitive maps based on adaptive graph learning for multivariate time series interpretable prediction. Appl Intell 55(12):888. https:\/\/doi.org\/10.1007\/s10489-025-06745-2","journal-title":"Appl Intell"},{"key":"9839_CR14","volume-title":"Advances in Neural Information Processing Systems","author":"G Ke","year":"2017","unstructured":"Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y (2017) Lightgbm: a highly efficient gradient boosting decision tree. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in neural information processing systems, vol 30. Curran Associates Inc, Long Beach CA, USA"},{"issue":"1","key":"9839_CR15","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/s0020-7373(86)80040-2","volume":"24","author":"B Kosko","year":"1986","unstructured":"Kosko B (1986) Fuzzy cognitive maps. Int J Man-Mach Stud 24(1):65\u201375. https:\/\/doi.org\/10.1016\/s0020-7373(86)80040-2","journal-title":"Int J Man-Mach Stud"},{"key":"9839_CR16","doi-asserted-by":"publisher","unstructured":"Kyunghyun C, Merrienboer B, Caglar G, Dzmitry B, Fethi B, Holger S, Yoshua B (2014) Learning phrase representations using RNN encoder\u2013decoder for statistical machine translation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), p 1724. https:\/\/doi.org\/10.3115\/v1\/D14-1179","DOI":"10.3115\/v1\/D14-1179"},{"issue":"8","key":"9839_CR17","doi-asserted-by":"publisher","first-page":"7732","DOI":"10.1109\/TCYB.2021.3049630","volume":"52","author":"J Li","year":"2021","unstructured":"Li J, Zhang C, Zhou JT, Fu H, Xia S, Hu Q (2021) Deep-lift: deep label-specific feature learning for image annotation. IEEE Trans Cybern 52(8):7732\u20137741. https:\/\/doi.org\/10.1109\/TCYB.2021.3049630","journal-title":"IEEE Trans Cybern"},{"issue":"4","key":"9839_CR18","doi-asserted-by":"publisher","first-page":"1748","DOI":"10.1016\/j.ijforecast.2021.03.012","volume":"37","author":"B Lim","year":"2021","unstructured":"Lim B, Ar\u0131k S\u00d6, Loeff N, Pfister T (2021) Temporal fusion transformers for interpretable multi-horizon time series forecasting. Int J Forecast 37(4):1748\u20131764. https:\/\/doi.org\/10.1016\/j.ijforecast.2021.03.012","journal-title":"Int J Forecast"},{"key":"9839_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-023-08977-1","author":"X Liu","year":"2023","unstructured":"Liu X, Zhang Y, Wang J, Qin J, Yin H, Yang Y, Huang H (2023) Time-series forecasting based on fuzzy cognitive maps and GRU-autoencoder. Soft Comput. https:\/\/doi.org\/10.1007\/s00500-023-08977-1","journal-title":"Soft Comput"},{"key":"9839_CR20","unstructured":"Lundberg SM, Lee S-I (2017) A Unified Approach to Interpreting Model Predictions. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in neural information processing systems 30. Curran Associates Inc, Long Beach CA, USA, pp 4765\u20134774"},{"key":"9839_CR21","doi-asserted-by":"publisher","first-page":"6835","DOI":"10.1007\/s00500-019-04321-8","volume":"24","author":"C Luo","year":"2020","unstructured":"Luo C, Zhang N, Wang X (2020) Time series prediction based on intuitionistic fuzzy cognitive map. Soft Comput 24:6835\u20136850. https:\/\/doi.org\/10.1007\/s00500-019-04321-8","journal-title":"Soft Comput"},{"issue":"4","key":"9839_CR22","doi-asserted-by":"publisher","first-page":"1346","DOI":"10.1016\/j.ijforecast.2021.11.013","volume":"38","author":"S Makridakis","year":"2022","unstructured":"Makridakis S, Spiliotis E, Assimakopoulos V (2022) M5 accuracy competition: results, findings, and conclusions. Int J Forecast 38(4):1346\u20131364. https:\/\/doi.org\/10.1016\/j.ijforecast.2021.11.013","journal-title":"Int J Forecast"},{"key":"9839_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117721","volume":"205","author":"A Morales-Hern\u00e1ndez","year":"2022","unstructured":"Morales-Hern\u00e1ndez A, N\u00e1poles G, Jastrzebska A, Salgueiro Y, Vanhoof K (2022) Online learning of windmill time series using long short-term cognitive networks. Expert Syst Appl 205:117721. https:\/\/doi.org\/10.1016\/j.eswa.2022.117721","journal-title":"Expert Syst Appl"},{"key":"9839_CR24","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.neunet.2019.03.012","volume":"115","author":"G N\u00e1poles","year":"2019","unstructured":"N\u00e1poles G, Vanhoenshoven F, Vanhoof K (2019) Short-term cognitive networks, flexible reasoning and nonsynaptic learning. Neural Netw 115:72\u201381. https:\/\/doi.org\/10.1016\/j.neunet.2019.03.012","journal-title":"Neural Netw"},{"issue":"19","key":"9839_CR25","doi-asserted-by":"publisher","first-page":"16959","DOI":"10.1007\/s00521-022-07348-5","volume":"34","author":"G N\u00e1poles","year":"2022","unstructured":"N\u00e1poles G, Grau I, Jastrzebska A, Salgueiro Y (2022) Long short-term cognitive networks. Neural Comput Appl 34(19):16959\u201316971. https:\/\/doi.org\/10.1007\/s00521-022-07348-5","journal-title":"Neural Comput Appl"},{"key":"9839_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111078","volume":"281","author":"G N\u00e1poles","year":"2023","unstructured":"N\u00e1poles G, Rankovi\u0107 N, Salgueiro Y (2023) On the interpretability of fuzzy cognitive maps. Knowl Based Syst 281:111078. https:\/\/doi.org\/10.1016\/j.knosys.2023.111078","journal-title":"Knowl Based Syst"},{"issue":"10","key":"9839_CR27","doi-asserted-by":"publisher","first-page":"6083","DOI":"10.1109\/TCYB.2022.3165104","volume":"53","author":"G N\u00e1poles","year":"2023","unstructured":"N\u00e1poles G, Salgueiro Y, Grau I, Espinosa ML (2023) Recurrence-aware long-term cognitive network for explainable pattern classification. IEEE Trans Cybern 53(10):6083\u20136094. https:\/\/doi.org\/10.1109\/TCYB.2022.3165104","journal-title":"IEEE Trans Cybern"},{"issue":"13s","key":"9839_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3583558","volume":"55","author":"M Nauta","year":"2023","unstructured":"Nauta M, Trienes J, Pathak S, Nguyen E, Peters M, Schmitt Y, Schl\u00f6tterer J, Van Keulen M, Seifert C (2023) From anecdotal evidence to quantitative evaluation methods: a systematic review on evaluating explainable AI. ACM Comput Surv 55(13s):1\u201342. https:\/\/doi.org\/10.1145\/3583558","journal-title":"ACM Comput Surv"},{"key":"9839_CR29","doi-asserted-by":"publisher","unstructured":"Orang O, Silva R, Silva PC, Guimar\u00e3es FG (2020) Solar energy forecasting with fuzzy time series using high-order fuzzy cognitive maps. In: 2020 IEEE international conference on fuzzy systems (FUZZ-IEEE), pp. 1\u20138. https:\/\/doi.org\/10.1109\/FUZZ48607.2020.9177767","DOI":"10.1109\/FUZZ48607.2020.9177767"},{"key":"9839_CR30","doi-asserted-by":"publisher","first-page":"7733","DOI":"10.1007\/s10462-022-10319-w","volume":"56","author":"O Orang","year":"2023","unstructured":"Orang O, Silva PC, Guimar\u00e3es FG (2023) Time series forecasting using fuzzy cognitive maps: a survey. Artif Intell Rev 56:7733\u20137794. https:\/\/doi.org\/10.1007\/s10462-022-10319-w","journal-title":"Artif Intell Rev"},{"key":"9839_CR31","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2024.3395833","author":"C Ouyang","year":"2024","unstructured":"Ouyang C, Yang F, Yu F, Pedrycz W, Homenda W, Chang J, He Q, Yang Z (2024) Constructing spatial relationship and temporal relationship oriented composite fuzzy cognitive maps for multivariate time series forecasting. IEEE Trans Fuzzy Syst. https:\/\/doi.org\/10.1109\/TFUZZ.2024.3395833","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"9839_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2025.112724","volume":"170","author":"C Ouyang","year":"2025","unstructured":"Ouyang C, Yu F, Yang F (2025) Equipping high-order fuzzy cognitive map with interpretable weights for multivariate time series forecasting. Appl Soft Comput 170:112724. https:\/\/doi.org\/10.1016\/j.asoc.2025.112724","journal-title":"Appl Soft Comput"},{"key":"9839_CR33","doi-asserted-by":"publisher","unstructured":"Pedrycz W (2021) The principle of justifiable granularity. Springer, Cham, p 147\u2013160. https:\/\/doi.org\/10.1007\/978-3-030-52800-3_10","DOI":"10.1007\/978-3-030-52800-3_10"},{"issue":"10","key":"9839_CR34","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. https:\/\/doi.org\/10.1016\/j.asoc.2013.06.017","journal-title":"Appl Soft Comput"},{"key":"9839_CR35","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2024.3433467","author":"Z Peng","year":"2024","unstructured":"Peng Z, Liu W, Oh S-K (2024) Afs-fcm with memory: a model for air quality multi-dimensional prediction with interpretability. IEEE Trans Big Data. https:\/\/doi.org\/10.1109\/TBDATA.2024.3433467","journal-title":"IEEE Transactions on Big Data"},{"key":"9839_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109586","volume":"129","author":"B Qiao","year":"2022","unstructured":"Qiao B, Liu J, Wu P, Teng Y (2022) Wind power forecasting based on variational mode decomposition and high-order fuzzy cognitive maps. Appl Soft Comput 129:109586. https:\/\/doi.org\/10.1016\/j.asoc.2022.109586","journal-title":"Appl Soft Comput"},{"key":"9839_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110700","volume":"275","author":"D Qin","year":"2023","unstructured":"Qin D, Peng Z, Wu L (2023) Deep attention fuzzy cognitive maps for interpretable multivariate time series prediction. Knowl Based Syst 275:110700. https:\/\/doi.org\/10.1016\/j.knosys.2023.110700","journal-title":"Knowl Based Syst"},{"key":"9839_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2024.110412","volume":"194","author":"D Qin","year":"2024","unstructured":"Qin D, Peng Z, Wu L (2024) Interpretable predictive modeling of non-stationary long time series. Comput Ind Eng 194:110412. https:\/\/doi.org\/10.1016\/j.cie.2024.110412","journal-title":"Comput Ind Eng"},{"key":"9839_CR39","doi-asserted-by":"publisher","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) Why should i trust you?: Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. KDD \u201916, vol. 13\u201317-Augu. ACM, New York, NY, USA, p 1135\u20131144. https:\/\/doi.org\/10.1145\/2939672.2939778","DOI":"10.1145\/2939672.2939778"},{"key":"9839_CR40","unstructured":"Schulz K, Sixt L, Tombari F, Landgraf T (2020) Restricting the flow: information bottlenecks for attribution. In: International conference on learning representations. https:\/\/openreview.net\/forum?id=S1xWh1rYwB"},{"key":"9839_CR41","doi-asserted-by":"publisher","unstructured":"Shan D, Lu W, Yang J (2018) The data-driven fuzzy cognitive map model and its application to prediction of time series. Int J Innov Comput Inf Control. https:\/\/doi.org\/10.24507\/ijicic.14.05.1583","DOI":"10.24507\/ijicic.14.05.1583"},{"issue":"8","key":"9839_CR42","doi-asserted-by":"publisher","first-page":"2336","DOI":"10.1109\/TFUZZ.2020.2998513","volume":"29","author":"F Shen","year":"2021","unstructured":"Shen F, Liu J, Wu K (2021) Multivariate time series forecasting based on elastic net and high-order fuzzy cognitive maps: a case study on human action prediction through EEG signals. IEEE Trans Fuzzy Syst 29(8):2336\u20132348. https:\/\/doi.org\/10.1109\/TFUZZ.2020.2998513","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"9839_CR43","doi-asserted-by":"crossref","unstructured":"\u0160imi\u0107 I, Sabol V, Veas E (2022) Perturbation effect: a metric to counter misleading validation of feature attribution. In: Proceedings of the 31st ACM international conference on information & knowledge management, p 1798\u20131807","DOI":"10.1145\/3511808.3557418"},{"key":"9839_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107271","volume":"105","author":"P Szwed","year":"2021","unstructured":"Szwed P (2021) Classification and feature transformation with fuzzy cognitive maps. Appl Soft Comput 105:107271. https:\/\/doi.org\/10.1016\/j.asoc.2021.107271","journal-title":"Appl Soft Comput"},{"issue":"5","key":"9839_CR45","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/s11063-024-11666-1","volume":"56","author":"Y Teng","year":"2024","unstructured":"Teng Y, Liu J, Wu K (2024) Time series prediction based on lstm and high-order fuzzy cognitive map with attention mechanism. Neural Process Lett 56(5):237. https:\/\/doi.org\/10.1007\/s11063-024-11666-1","journal-title":"Neural Process Lett"},{"key":"9839_CR46","doi-asserted-by":"publisher","first-page":"2021","DOI":"10.1007\/s41066-023-00417-7","volume":"8","author":"M Tyrovolas","year":"2023","unstructured":"Tyrovolas M, Liang XS, Stylios C (2023) Information flow-based fuzzy cognitive maps with enhanced interpretability. Granul Comput 8:2021\u20132038. https:\/\/doi.org\/10.1007\/s41066-023-00417-7","journal-title":"Granul Comput"},{"key":"9839_CR47","doi-asserted-by":"publisher","unstructured":"Vanhoenshoven F, N\u00e1poles G, Bielen S, Vanhoof K (2018) Fuzzy cognitive maps employing ARIMA components for time series forecasting. In: Czarnowski I, Howlett RJ, Jain LC (eds) Intelligent decision technologies 2017. Springer, Cham, p 255\u2013264. https:\/\/doi.org\/10.1007\/978-3-319-59421-7_24","DOI":"10.1007\/978-3-319-59421-7_24"},{"key":"9839_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106461","volume":"95","author":"F Vanhoenshoven","year":"2020","unstructured":"Vanhoenshoven F, N\u00e1poles G, Froelich W, Salmeron JL, Vanhoof K (2020) Pseudoinverse learning of fuzzy cognitive maps for multivariate time series forecasting. Appl Soft Comput 95:106461","journal-title":"Appl Soft Comput"},{"issue":"29","key":"9839_CR49","doi-asserted-by":"publisher","first-page":"41611","DOI":"10.1007\/s11042-021-11007-7","volume":"81","author":"J Wang","year":"2021","unstructured":"Wang J, Lu S, Wang S-H, Zhang Y-D (2021) A review on extreme learning machine. Multimed Tools Appl 81(29):41611\u201341660. https:\/\/doi.org\/10.1007\/s11042-021-11007-7","journal-title":"Multimed Tools Appl"},{"issue":"9","key":"9839_CR50","doi-asserted-by":"publisher","first-page":"2647","DOI":"10.1109\/TFUZZ.2020.3005293","volume":"29","author":"J Wang","year":"2021","unstructured":"Wang J, Peng Z, Wang X, Li C, Wu J (2021) Deep fuzzy cognitive maps for interpretable multivariate time series prediction. IEEE Trans Fuzzy Syst 29(9):2647\u20132660. https:\/\/doi.org\/10.1109\/TFUZZ.2020.3005293","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"12","key":"9839_CR51","doi-asserted-by":"publisher","first-page":"5166","DOI":"10.1109\/TFUZZ.2022.3169624","volume":"30","author":"Y Wang","year":"2022","unstructured":"Wang Y, Yu F, Homenda W, Pedrycz W, Tang Y, Jastrz\u0119bska A, Li F (2022) The trend-fuzzy-granulation-based adaptive fuzzy cognitive map for long-term time series forecasting. IEEE Trans Fuzzy Syst 30(12):5166\u20135180. https:\/\/doi.org\/10.1109\/TFUZZ.2022.3169624","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"9839_CR52","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1109\/TLT.2023.3307565","volume":"17","author":"Y Wei","year":"2024","unstructured":"Wei Y, Jiang B (2024) Interpretable cognitive state prediction via temporal fuzzy cognitive map. IEEE Transactions on Learning Technologies 17:514\u2013526. https:\/\/doi.org\/10.1109\/TLT.2023.3307565","journal-title":"IEEE Transactions on Learning Technologies"},{"key":"9839_CR53","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1007\/978-1-4612-4380-9_16","volume":"1","author":"F Wilcoxon","year":"1945","unstructured":"Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics 1:196\u2013202. https:\/\/doi.org\/10.1007\/978-1-4612-4380-9_16","journal-title":"Biometrics"},{"issue":"12","key":"9839_CR54","doi-asserted-by":"publisher","first-page":"3110","DOI":"10.1109\/TFUZZ.2019.2956904","volume":"28","author":"K Wu","year":"2020","unstructured":"Wu K, Liu J, Liu P, Yang S (2020) Time series prediction using sparse autoencoder and high-order fuzzy cognitive maps. IEEE Trans Fuzzy Syst 28(12):3110\u20133121. https:\/\/doi.org\/10.1109\/TFUZZ.2019.2956904","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"9839_CR55","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/s00500-021-06455-0","volume":"26","author":"Y Xixi","year":"2022","unstructured":"Xixi Y, Fengqian D, Chao L (2022) Time series prediction based on high-order intuitionistic fuzzy cognitive maps with variational mode decomposition. Soft Comput 26:189\u2013201. https:\/\/doi.org\/10.1007\/s00500-021-06455-0","journal-title":"Soft Comput"},{"issue":"6","key":"9839_CR56","doi-asserted-by":"publisher","first-page":"3391","DOI":"10.1109\/TFUZZ.2018.2831640","volume":"26","author":"S Yang","year":"2018","unstructured":"Yang S, Liu J (2018) Time-series forecasting based on high-order fuzzy cognitive maps and wavelet transform. IEEE Trans Fuzzy Syst 26(6):3391\u20133402. https:\/\/doi.org\/10.1109\/TFUZZ.2018.2831640","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"9839_CR57","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2024.3474476","author":"F Yang","year":"2024","unstructured":"Yang F, Yu F, Ouyang C, Tang Y (2024) Design trend fuzzy granulation-based three-layer fuzzy cognitive map for long-term forecasting of multivariate time series. IEEE Trans Fuzzy Syst. https:\/\/doi.org\/10.1109\/TFUZZ.2024.3474476","journal-title":"IEEE Trans Fuzzy Syst"}],"container-title":["Evolving Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-026-09839-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12530-026-09839-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-026-09839-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T04:51:05Z","timestamp":1781153465000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12530-026-09839-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,11]]},"references-count":57,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["9839"],"URL":"https:\/\/doi.org\/10.1007\/s12530-026-09839-5","relation":{},"ISSN":["1868-6478","1868-6486"],"issn-type":[{"value":"1868-6478","type":"print"},{"value":"1868-6486","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,11]]},"assertion":[{"value":"19 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 May 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 June 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"77"}}