{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:29:32Z","timestamp":1763202572054,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030922696"},{"type":"electronic","value":"9783030922702"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-92270-2_3","type":"book-chapter","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T11:06:00Z","timestamp":1638788760000},"page":"28-39","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Multi-Reservoir Echo State Network with\u00a0Multiple-Size Input Time Slices for\u00a0Nonlinear Time-Series Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7208-9003","authenticated-orcid":false,"given":"Ziqiang","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6223-4406","authenticated-orcid":false,"given":"Gouhei","family":"Tanaka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,12,7]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Gallicchio, C., Micheli, A.: A Markovian characterization of redundancy in echo state networks by PCA. In: ESANN. Citeseer (2010)","DOI":"10.1109\/IJCNN.2010.5596796"},{"key":"3_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1007\/978-3-030-20521-8_40","volume-title":"Advances in Computational Intelligence","author":"C Gallicchio","year":"2019","unstructured":"Gallicchio, C., Micheli, A.: Richness of deep echo state network dynamics. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2019. LNCS, vol. 11506, pp. 480\u2013491. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20521-8_40"},{"key":"3_CR4","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.neucom.2016.12.089","volume":"268","author":"C Gallicchio","year":"2017","unstructured":"Gallicchio, C., Micheli, A., Pedrelli, L.: Deep reservoir computing: a critical experimental analysis. Neurocomputing 268, 87\u201399 (2017)","journal-title":"Neurocomputing"},{"issue":"8","key":"3_CR5","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)","journal-title":"Neural Comput."},{"key":"3_CR6","unstructured":"Jaeger, H.: The \u201cecho state\u201d approach to analysing and training recurrent neural networks-with an erratum note. Ger. Natl. Res. Cent. Inf. Technol. GMD Tech. Rep. 148(34), 13 (2001)"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Li, Z., Tanaka, G.: Deep echo state networks with multi-span features for nonlinear time series prediction. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20139. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9207401"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Li, Z., Tanaka, G.: HP-ESN: echo state networks combined with hodrick-prescott filter for nonlinear time-series prediction. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20139. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9206771"},{"key":"3_CR9","doi-asserted-by":"publisher","first-page":"113082","DOI":"10.1016\/j.eswa.2019.113082","volume":"143","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Gong, C., Yang, L., Chen, Y.: DSTP-RNN: a dual-stage two-phase attentionbased recurrent neural network for long-term and multivariate time series prediction. Expert Syst. Appl. 143, 113082 (2020)","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"3_CR10","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1175\/1520-0469(1963)020<0130:DNF>2.0.CO;2","volume":"20","author":"EN Lorenz","year":"1963","unstructured":"Lorenz, E.N.: Deterministic nonperiodic flow. J. Atmos. Sci. 20(2), 130\u2013141 (1963)","journal-title":"J. Atmos. Sci."},{"issue":"4300","key":"3_CR11","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1126\/science.267326","volume":"197","author":"MC Mackey","year":"1977","unstructured":"Mackey, M.C., Glass, L.: Oscillation and chaos in physiological control systems. Science 197(4300), 287\u2013289 (1977)","journal-title":"Science"},{"key":"3_CR12","doi-asserted-by":"publisher","DOI":"10.1201\/9781420049176","volume-title":"Recurrent Neural Networks: Design and Applications","author":"L Medsker","year":"1999","unstructured":"Medsker, L., Jain, L.C.: Recurrent Neural Networks: Design and Applications. CRC Press, Boca Raton (1999)"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Menezes, J.M., Barreto, G.A.: A new look at nonlinear time series prediction with narx recurrent neural network. In: 2006 Ninth Brazilian Symposium on Neural Networks (SBRN 2006), pp. 160\u2013165. IEEE (2006)","DOI":"10.1109\/SBRN.2006.7"},{"key":"3_CR14","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1016\/j.asoc.2017.10.038","volume":"62","author":"L Shen","year":"2018","unstructured":"Shen, L., Chen, J., Zeng, Z., Yang, J., Jin, J.: A novel echo state network for multivariate and nonlinear time series prediction. Appl. Soft Comput. 62, 524\u2013535 (2018)","journal-title":"Appl. Soft Comput."},{"key":"3_CR15","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.neucom.2019.09.115","volume":"406","author":"Z Song","year":"2020","unstructured":"Song, Z., Wu, K., Shao, J.: Destination prediction using deep echo state network. Neurocomputing 406, 343\u2013353 (2020)","journal-title":"Neurocomputing"},{"key":"3_CR16","doi-asserted-by":"publisher","unstructured":"Suykens, J.A., Vandewalle, J.: The KU leuven time series prediction competition. In: Suykens J.A.K., Vandewalle J. (eds) Nonlinear Modeling, pp. 241\u2013253. Springer, Boston (1998). https:\/\/doi.org\/10.1007\/978-1-4615-5703-6_9","DOI":"10.1007\/978-1-4615-5703-6_9"},{"key":"3_CR17","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.neunet.2019.03.005","volume":"115","author":"G Tanaka","year":"2019","unstructured":"Tanaka, G., et al.: Recent advances in physical reservoir computing: a review. Neural Netw. 115, 100\u2013123 (2019)","journal-title":"Neural Netw."},{"key":"3_CR18","volume-title":"Numerical Methods for the Solution of III-Posed Problems","author":"AN Tikhonov","year":"2013","unstructured":"Tikhonov, A.N., Goncharsky, A., Stepanov, V., Yagola, A.G.: Numerical Methods for the Solution of III-Posed Problems, vol. 328. Springer Science & Business Media, Berlin (2013)"},{"key":"3_CR19","unstructured":"Weigend, A.S.: Time Series Prediction: Forecasting The Future And Understanding The Past Routledge, Abingdon-on-Thames (2018)"},{"key":"3_CR20","unstructured":"Yu, Z., Liu, G.: Sliced recurrent neural networks. arXiv preprint arXiv:1807.02291 (2018)"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-92270-2_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T15:57:55Z","timestamp":1710259075000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-92270-2_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030922696","9783030922702"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-92270-2_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 December 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sanur, Bali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2021.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1093","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"226","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"177","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.57","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic the conference was held online.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}