{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T19:37:49Z","timestamp":1780083469786,"version":"3.54.0"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031777370","type":"print"},{"value":"9783031777387","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-77738-7_26","type":"book-chapter","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T16:41:14Z","timestamp":1732034474000},"page":"315-327","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Comparing MAE and\u00a0RMSE as\u00a0Fitness of\u00a0Genetic Algorithm for\u00a0Optimizing Echo State Network Hyperparameters with\u00a0Different Probabilistic Distributions"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1896-446X","authenticated-orcid":false,"given":"Henrique Vaz","family":"de Ara\u00fajo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2842-0387","authenticated-orcid":false,"given":"Fabian Corr\u00eaa","family":"Cardoso","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3512-6290","authenticated-orcid":false,"given":"Viviane Leite Dias","family":"de Mattos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1595-7676","authenticated-orcid":false,"given":"Eduardo Nunes","family":"Borges","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3776-0260","authenticated-orcid":false,"given":"Giancarlo","family":"Lucca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6996-7602","authenticated-orcid":false,"given":"Bruno Lopes","family":"Dalmazo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5125-2756","authenticated-orcid":false,"given":"Rafael Alceste","family":"Berri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,14]]},"reference":[{"issue":"1","key":"26_CR1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.ins.2003.03.023","volume":"170","author":"G Armano","year":"2005","unstructured":"Armano, G., Marchesi, M., Murru, A.: A hybrid genetic-neural architecture for stock indexes forecasting. Inf. Sci. 170(1), 3\u201333 (2005)","journal-title":"Inf. Sci."},{"key":"26_CR2","doi-asserted-by":"publisher","unstructured":"Cardoso, F.C., Berri, R.A., Borges, E.N., Dalmazo, B.L., Lucca, G., de\u00a0Mattos, V.L.D.: Echo state network and classical statistical techniques for time series forecasting: a review. Knowl.-Based Syst. 111639 (2024). https:\/\/doi.org\/10.1016\/j.knosys.2024.111639","DOI":"10.1016\/j.knosys.2024.111639"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Cardoso, F.C., et al.: Bovdb: a data set of stock prices of all companies in B3 from 1995 to 2020. J. Inf. Data Manag. 13(1) (2022)","DOI":"10.5753\/jidm.2022.2345"},{"key":"26_CR4","unstructured":"Casella, G., Berger, R.L.: Infer\u00eancia estat\u00edstica. Cengage Learning (2010)"},{"key":"26_CR5","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1007\/11540007_137","volume-title":"Fuzzy Systems and Knowledge Discovery","author":"Y Chen","year":"2005","unstructured":"Chen, Y., Abraham, A., Yang, J., Yang, B.: Hybrid methods for stock index modeling. In: Wang, L., Jin, Y. (eds.) FSKD 2005. LNCS (LNAI), vol. 3614, pp. 1067\u20131070. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11540007_137"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Chung, H., Shin, K.S.: Genetic algorithm-optimized long short-term memory network for stock market prediction. Sustainability 10(10), 3765 (2018)","DOI":"10.3390\/su10103765"},{"key":"26_CR7","unstructured":"Gallicchio, C., Micheli, A.: Deep echo state network (deepesn): a brief survey. arXiv preprint arXiv:1712.04323 (2017)"},{"key":"26_CR8","first-page":"4675","volume":"38","author":"F Hamad","year":"2023","unstructured":"Hamad, F., Younus, N., Muftah, M.M., Jaber, M.: Specify under-lining distribution for clustering linearly separable data: normal and uniform distribution case. J. Data Acquisition Process. 38, 4675 (2023)","journal-title":"J. Data Acquisition Process."},{"key":"26_CR9","doi-asserted-by":"crossref","unstructured":"Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Cambridge (1992)","DOI":"10.7551\/mitpress\/1090.001.0001"},{"key":"26_CR10","unstructured":"Jaeger, H.: The \u201cecho state\u201d approach to analysing and training recurrent neural networks-with an erratum note. German National Research Center for Information Technology GMD Technical Report, Bonn, Germany, vol. 148, no. 34, p. 13 (2001)"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., Tibshirani, R., et\u00a0al.: An Introduction to Statistical Learning, vol.\u00a0112. Springer, Heidelberg (2013)","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Luko\u0161evi\u010dius, M., Uselis, A.: Efficient implementations of echo state network cross-validation. Cogn. Comput. 1\u201315 (2021)","DOI":"10.1007\/s12559-021-09849-2"},{"key":"26_CR13","unstructured":"Morettin, P.A.: Econometria financeira: um curso em s\u00e9ries temporais financeiras. Editora Blucher (2017)"},{"key":"26_CR14","first-page":"13","volume":"6","author":"S Nayak","year":"2014","unstructured":"Nayak, S., Misra, B.B., Behera, H.S.: Impact of data normalization on stock index forecasting. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 6, 13\u201313 (2014)","journal-title":"Int. J. Comput. Inf. Syst. Ind. Manag. Appl."},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Nelson, D.B.: Conditional heteroskedasticity in asset returns: a new approach. Econometrica J. Econom. Soc. 347\u2013370 (1991)","DOI":"10.2307\/2938260"},{"key":"26_CR16","doi-asserted-by":"publisher","first-page":"6303","DOI":"10.1007\/s11227-019-02851-4","volume":"75","author":"HM Nguyen","year":"2019","unstructured":"Nguyen, H.M., Kalra, G., Jun, T.J., Woo, S., Kim, D.: Esnemble: an echo state network-based ensemble for workload prediction and resource allocation of web applications in the cloud. J. Supercomput. 75, 6303\u20136323 (2019)","journal-title":"J. Supercomput."},{"key":"26_CR17","unstructured":"Peterson, B.G., et al.: Package \u201cperformanceanalytics.\u201d R Team Cooperation 3, 13\u201314 (2018)"},{"issue":"6","key":"26_CR18","doi-asserted-by":"publisher","first-page":"1456","DOI":"10.1109\/72.728395","volume":"9","author":"EW Saad","year":"1998","unstructured":"Saad, E.W., Prokhorov, D.V., Wunsch, D.C.: Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Trans. Neural Networks 9(6), 1456\u20131470 (1998)","journal-title":"IEEE Trans. Neural Networks"},{"issue":"1","key":"26_CR19","first-page":"57","volume":"4","author":"S Saigal","year":"2012","unstructured":"Saigal, S., Mehrotra, D.: Performance comparison of time series data using predictive data mining techniques. Adv. Inf. Mining 4(1), 57\u201366 (2012)","journal-title":"Adv. Inf. Mining"},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Scrucca, L.: On some extensions to GA package: hybrid optimisation, parallelisation and islands evolution. arXiv preprint arXiv:1605.01931 (2016)","DOI":"10.32614\/RJ-2017-008"},{"issue":"3","key":"26_CR21","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.1109\/23.589532","volume":"44","author":"J Sola","year":"1997","unstructured":"Sola, J., Sevilla, J.: Importance of input data normalization for the application of neural networks to complex industrial problems. IEEE Trans. Nucl. Sci. 44(3), 1464\u20131468 (1997)","journal-title":"IEEE Trans. Nucl. Sci."},{"issue":"3","key":"26_CR22","doi-asserted-by":"publisher","first-page":"509","DOI":"10.32604\/csse.2021.014189","volume":"36","author":"G Sun","year":"2021","unstructured":"Sun, G., et al.: Stock price forecasting: an echo state network approach. Comput. Syst. Sci. Eng. 36(3), 509\u2013520 (2021)","journal-title":"Comput. Syst. Sci. Eng."},{"key":"26_CR23","doi-asserted-by":"crossref","unstructured":"Villanueva, R.A.M., Chen, Z.J.: ggplot2: elegant graphics for data analysis (2019)","DOI":"10.1080\/15366367.2019.1565254"},{"key":"26_CR24","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.neunet.2020.02.016","volume":"126","author":"PR Vlachas","year":"2020","unstructured":"Vlachas, P.R., et al.: Backpropagation algorithms and reservoir computing in recurrent neural networks for the forecasting of complex spatiotemporal dynamics. Neural Netw. 126, 191\u2013217 (2020)","journal-title":"Neural Netw."},{"issue":"2","key":"26_CR25","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.jeconom.2010.01.013","volume":"157","author":"D Zhu","year":"2010","unstructured":"Zhu, D., Galbraith, J.W.: A generalized asymmetric student-t distribution with application to financial econometrics. J. Econom. 157(2), 297\u2013305 (2010)","journal-title":"J. Econom."},{"key":"26_CR26","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.ins.2012.09.017","volume":"221","author":"H Zhu","year":"2013","unstructured":"Zhu, H., Leung, H., He, Z.: A variational bayesian approach to robust sensor fusion based on student-t distribution. Inf. Sci. 221, 201\u2013214 (2013)","journal-title":"Inf. Sci."}],"container-title":["Lecture Notes in Computer Science","Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-77738-7_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T16:50:16Z","timestamp":1732035016000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-77738-7_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,14]]},"ISBN":["9783031777370","9783031777387"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-77738-7_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,14]]},"assertion":[{"value":"14 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IDEAL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Data Engineering and Automated Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Valencia","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ideal2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}