{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T11:51:48Z","timestamp":1769773908886,"version":"3.49.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032159861","type":"print"},{"value":"9783032159878","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-15987-8_28","type":"book-chapter","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:27:39Z","timestamp":1769718459000},"page":"424-438","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced Forecasting Model Using Transformers, Extended Long-Short-Term Memory, and\u00a0Randomized Fuzzy Cognitive Maps"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4077-3775","authenticated-orcid":false,"given":"Omid","family":"Orang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1202-2552","authenticated-orcid":false,"given":"Petr\u00f4nio C. L.","family":"Silva","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9238-8839","authenticated-orcid":false,"given":"Frederico G.","family":"Guimar\u00e3es","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"key":"28_CR1","doi-asserted-by":"crossref","unstructured":"Bose, M., Mali, K.: Designing fuzzy time series forecasting models: a survey. Int. J. Approx. Reason. 111, 78\u201399 (2019)","DOI":"10.1016\/j.ijar.2019.05.002"},{"key":"28_CR2","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.: Deep attention fuzzy cognitive maps for interpretable multivariate time series prediction. Knowl.-Based Syst. 275, 110700 (2023)","journal-title":"Knowl.-Based Syst."},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Kim, J., Kim, H., Kim, H., Lee, D., Yoon, S.: A comprehensive survey of time series forecasting: architectural diversity and open challenges, arXiv preprint arXiv:2411.05793 (2024)","DOI":"10.1007\/s10462-025-11223-9"},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Mohammadi, H.A., Ghofrani, S., Nikseresht, A.: Using empirical wavelet transform and high-order fuzzy cognitive maps for time series forecasting. Appl. Soft Comput. 135, 109990 (2023). https:\/\/www.sciencedirect.com\/science\/article\/pii\/S156849462300008X","DOI":"10.1016\/j.asoc.2023.109990"},{"key":"28_CR5","doi-asserted-by":"crossref","unstructured":"Kosko, B.: Fuzzy cognitive maps. Int. J. Man-Mach. Stud. 24(1), 65\u201375 (1986). http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0020737386800402","DOI":"10.1016\/S0020-7373(86)80040-2"},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Orang, O., de Lima e Silva, P.C., Guimar\u00e3es, F.G.: Time series forecasting using fuzzy cognitive maps: a survey. Artif. Intell. Rev. 1\u201362 (2022)","DOI":"10.1007\/s10462-022-10319-w"},{"key":"28_CR7","doi-asserted-by":"publisher","first-page":"09","DOI":"10.1007\/s11063-024-11666-1","volume":"56","author":"Y Teng","year":"2024","unstructured":"Teng, Y., Liu, J., Wu, K.: Time series prediction based on LSTM and high-order fuzzy cognitive map with attention mechanism. Neural Process. Lett. 56, 09 (2024)","journal-title":"Neural Process. Lett."},{"key":"28_CR8","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1016\/j.knosys.2014.07.004","volume":"70","author":"W Lu","year":"2014","unstructured":"Lu, W., Yang, J., Liu, X., Pedrycz, W.: The modeling and prediction of time series based on synergy of high-order fuzzy cognitive map and fuzzy C-means clustering. Knowl.-Based Syst. 70, 242\u2013255 (2014)","journal-title":"Knowl.-Based Syst."},{"issue":"6","key":"28_CR9","doi-asserted-by":"publisher","first-page":"3391","DOI":"10.1109\/TFUZZ.2018.2831640","volume":"26","author":"Y Shanchao","year":"2018","unstructured":"Shanchao, Y., Liu, J.: Time-series forecasting based on high-order fuzzy cognitive maps and wavelet transform. IEEE Trans. Fuzzy Syst. 26(6), 3391\u20133402 (2018)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"28_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106359","volume":"206","author":"K Yuan","year":"2020","unstructured":"Yuan, K., Liu, J., Yang, S., Wu, K., Shen, F.: Time series forecasting based on kernel mapping and high-order fuzzy cognitive maps. Knowl.-Based Syst. 206, 106359 (2020)","journal-title":"Knowl.-Based Syst."},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"Liu, Z., Liu, J.: A robust time series prediction method based on empirical mode decomposition and high-order fuzzy cognitive maps. Knowl.-Based Syst. 203, 106105 (2020). https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0950705120303713","DOI":"10.1016\/j.knosys.2020.106105"},{"issue":"12","key":"28_CR12","doi-asserted-by":"publisher","first-page":"3110","DOI":"10.1109\/TFUZZ.2019.2956904","volume":"28","author":"K Wu","year":"2019","unstructured":"Wu, K., Liu, J., Liu, P., Yang, S.: Time series prediction using sparse autoencoder and high-order fuzzy cognitive maps. IEEE Trans. Fuzzy Syst. 28(12), 3110\u20133121 (2019)","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"9","key":"28_CR13","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.: Deep fuzzy cognitive maps for interpretable multivariate time series prediction. IEEE Trans. Fuzzy Syst. 29(9), 2647\u20132660 (2021)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"28_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.108447","volume":"117","author":"F Ding","year":"2022","unstructured":"Ding, F., Luo, C.: Interpretable cognitive learning with spatial attention for high-volatility time series prediction. Appl. Soft Comput. 117, 108447 (2022)","journal-title":"Appl. Soft Comput."},{"key":"28_CR15","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS 2017, pp. 6000\u20136010. Curran Associates Inc., Red Hook (2017)"},{"key":"28_CR16","unstructured":"Beck, M., et al.: xLSTM: extended long short-term memory, arXiv, vol. abs\/2405.04517 (2024). https:\/\/api.semanticscholar.org\/CorpusID:269614336"},{"key":"28_CR17","doi-asserted-by":"crossref","unstructured":"Orang, O., de Lima e Silva, P.C., Silva, R., Guimar\u00e3es, F.G.: Randomized high order fuzzy cognitive maps as reservoir computing models: a first introduction and applications. Neurocomputing 512, 153\u2013177 (2022). https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925231222011171","DOI":"10.1016\/j.neucom.2022.09.030"},{"key":"28_CR18","doi-asserted-by":"crossref","unstructured":"Sun, F., Hao, W., Zou, A., Shen, Q.: A survey on spatio-temporal series prediction with deep learning: taxonomy, applications, and future directions. Neural Comput. Appl. 1\u201325 (2024)","DOI":"10.1007\/s00521-024-09659-1"},{"issue":"8","key":"28_CR19","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.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997). https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput."},{"key":"28_CR20","unstructured":"Orang, O.: High-order fuzzy cognitive maps and randomized networks for time series and nonlinear dynamical systems (2023)"},{"key":"28_CR21","doi-asserted-by":"crossref","unstructured":"Orang, O., Bitencourt, H.V., de Souza, L.A.F., de Oliveira Lucas, P., Silva, P.C., Guimar\u00e3es, F.G.: Multiple-input\u2013multiple-output randomized fuzzy cognitive map method for high-dimensional time series forecasting. IEEE Trans. Fuzzy Syst. 32(6), 3703\u20133715 (2024)","DOI":"10.1109\/TFUZZ.2024.3379853"},{"key":"28_CR22","doi-asserted-by":"crossref","unstructured":"Orang, O., Erazo-Costa, F.J., Silva, P.C.L., de Alencar Barreto, G., Guimar\u00e3es, F.G.: A large reservoir computing forecasting method based on randomized fuzzy cognitive maps. In: 2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), pp. 1\u20138 (2024)","DOI":"10.1109\/EAIS58494.2024.10570027"},{"key":"28_CR23","doi-asserted-by":"crossref","unstructured":"Orang, O., de Lima e Silva, P.C., Guimar\u00e3es, F.G.: Multi-output time series forecasting with randomized multivariate fuzzy cognitive maps. Chaos Solitons Fractals 176, 114077 (2023). https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0960077923009785","DOI":"10.1016\/j.chaos.2023.114077"},{"key":"28_CR24","unstructured":"Guimaraes, F.G., Sadaei, H.J.: Data for: short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series. https:\/\/data.mendeley.com\/datasets\/f4fcrh4tn9\/1"},{"key":"28_CR25","doi-asserted-by":"crossref","unstructured":"Sadaei, H.J., de Lima e Silva, P.C., Guimar\u00e3es, F.G., Lee, M.H.: Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series. Energy 175, 365\u2013377 (2019). https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0360544219304852","DOI":"10.1016\/j.energy.2019.03.081"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-15987-8_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:27:43Z","timestamp":1769718463000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-15987-8_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032159861","9783032159878"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-15987-8_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"30 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Fortaleza-CE","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/bracis.sbc.org.br\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}