{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T03:23:47Z","timestamp":1790738627613,"version":"4.1.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2020,4,30]],"date-time":"2020-04-30T00:00:00Z","timestamp":1588204800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,4,30]],"date-time":"2020-04-30T00:00:00Z","timestamp":1588204800000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2020,12]]},"DOI":"10.1007\/s00521-020-04948-x","type":"journal-article","created":{"date-parts":[[2020,4,29]],"date-time":"2020-04-29T22:02:45Z","timestamp":1588197765000},"page":"17769-17787","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":95,"title":["Time series prediction using deep echo state networks"],"prefix":"10.1007","volume":"32","author":[{"given":"Taehwan","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian R.","family":"King","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,4,30]]},"reference":[{"issue":"3\u20134","key":"4948_CR1","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1089\/10665270360688057","volume":"10","author":"Z Bar-Joseph","year":"2003","unstructured":"Bar-Joseph Z, Gerber GK, Gifford DK, Jaakkola TS, Simon I (2003) Continuous representations of time-series gene expression data. J Comput Biol 10(3\u20134):341\u2013356","journal-title":"J Comput Biol"},{"key":"4948_CR2","doi-asserted-by":"crossref","unstructured":"Taylor SJ (2007) Modeling financial time series","DOI":"10.1142\/6578"},{"key":"4948_CR3","volume-title":"Time-series analysis: a comprehensive introduction for social scientists","author":"JM Gottman","year":"1981","unstructured":"Gottman JM (1981) Time-series analysis: a comprehensive introduction for social scientists, vol 400. Cambridge University Press, Cambridge"},{"issue":"9","key":"4948_CR4","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1016\/0026-2714(95)00154-9","volume":"36","author":"R Billinton","year":"1996","unstructured":"Billinton R, Chen H, Ghajar R (1996) Time-series models for reliability evaluation of power systems including wind energy. Microelectron Reliab 36(9):1253\u20131261","journal-title":"Microelectron Reliab"},{"key":"4948_CR5","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1038\/350324a0","volume":"350","author":"M Ghil","year":"1991","unstructured":"Ghil M, Vautard R (1991) Interdecadal oscillations and the warming trend in global temperature time series. Nature 350:324\u2013327","journal-title":"Nature"},{"issue":"2","key":"4948_CR6","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1007\/s00521-004-0413-4","volume":"13","author":"I Maqsood","year":"2004","unstructured":"Maqsood I, Khan MR, Abraham A (2004) An ensemble of neural networks for weather forecasting. Neural Comput Appl 13(2):112\u2013122","journal-title":"Neural Comput Appl"},{"issue":"3","key":"4948_CR7","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1109\/TPWRS.2002.800906","volume":"17","author":"JW Taylor","year":"2002","unstructured":"Taylor JW, Buizza R (2002) Neural network load forecasting with weather ensemble predictions. IEEE Trans Power Syst 17(3):626\u2013632","journal-title":"IEEE Trans Power Syst"},{"issue":"3","key":"4948_CR8","doi-asserted-by":"publisher","first-page":"7313","DOI":"10.1016\/j.eswa.2008.09.049","volume":"36","author":"X Lin","year":"2009","unstructured":"Lin X, Yang Z, Song Y (2009) Short-term stock price prediction based on echo state networks. Expert Syst Appl 36(3):7313\u20137317","journal-title":"Expert Syst Appl"},{"key":"4948_CR9","unstructured":"Bernal A, Fok S, Pidaparthi R (2012) Financial market time series prediction with recurrent neural networks the challenge of time series prediction echo state network implementation, pp 1\u20135"},{"key":"4948_CR10","unstructured":"Jaeger H (2001) The \u201cecho state\u201d approach to analysing and training recurrent neural networks-with an erratum note. German National Research Institute for Computer Science"},{"key":"4948_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(01)00702-0","author":"PG Zhang","year":"2003","unstructured":"Zhang PG (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing. https:\/\/doi.org\/10.1016\/S0925-2312(01)00702-0","journal-title":"Neurocomputing"},{"key":"4948_CR12","first-page":"29","volume":"1994","author":"GP Nason","year":"2006","unstructured":"Nason GP (2006) Stationary and non-stationary time series. Stat Volcanol 1994:29","journal-title":"Stat Volcanol"},{"key":"4948_CR13","unstructured":"Palachy S (2019) Stationarity in time series analysis. Towards Data Science. https:\/\/towardsdatascience.com\/stationarity-in-time-series-analysis-90c94f27322"},{"key":"4948_CR14","unstructured":"Zaiontz C (2018) Dickey\u2013Fuller test. http:\/\/www.real-statistics.com\/time-series-analysis\/stochastic-processes\/dickey-fuller-test\/"},{"key":"4948_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/j.1467-9892.2009.00647.x","volume":"3","author":"GN Boshnakov","year":"2010","unstructured":"Boshnakov GN (2010) Introductory time series with R. J Time Ser Anal 3:1. https:\/\/doi.org\/10.1111\/j.1467-9892.2009.00647.x","journal-title":"J Time Ser Anal"},{"issue":"2","key":"4948_CR16","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1142\/S0218488598000094","volume":"6","author":"S Hochreiter","year":"1998","unstructured":"Hochreiter S (1998) The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int J Uncertainty Fuzziness Knowl Based Syst 6(2):107\u2013116. https:\/\/doi.org\/10.1142\/S0218488598000094","journal-title":"Int J Uncertainty Fuzziness Knowl Based Syst"},{"key":"4948_CR17","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1197\/jamia.M2577","volume":"2","author":"F Cummins","year":"2000","unstructured":"Cummins F, Gers FA, Schmidhuber J (2000) Learning to forget: continual prediction with LSTM. Neural Comput 2:850\u2013855. https:\/\/doi.org\/10.1197\/jamia.M2577","journal-title":"Neural Comput"},{"key":"4948_CR18","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling, pp 1\u20139. Retrieved from http:\/\/arxiv.org\/abs\/1412.3555"},{"issue":"3","key":"4948_CR19","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/J.COSREV.2009.03.005","volume":"3","author":"M Luko\u0161evi\u010dius","year":"2009","unstructured":"Luko\u0161evi\u010dius M, Jaeger H (2009) Reservoir computing approaches to recurrent neural network training. Comput Sci Rev 3(3):127\u2013149. https:\/\/doi.org\/10.1016\/J.COSREV.2009.03.005","journal-title":"Comput Sci Rev"},{"key":"4948_CR20","volume-title":"Neural networks: tricks of the trade. Lecture Notes in Computer Science","author":"M Luko\u0161evi\u010dius","year":"2012","unstructured":"Luko\u0161evi\u010dius M (2012) A practical guide to applying Echo State Networks. In: Montavon G, Orr GB, M\u00fcller KR (eds) Neural networks: tricks of the trade. Lecture Notes in Computer Science, vol 7700. Springer, Berlin"},{"key":"4948_CR21","unstructured":"Gallicchio C, Micheli A, Pedrelli L (2018) Deep Echo State Networks for diagnosis of Parkinson\u2019s disease. Retrieved from http:\/\/arxiv.org\/abs\/1802.06708"},{"issue":"5","key":"4948_CR22","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1109\/TNNLS.2012.2188414","volume":"23","author":"D Li","year":"2012","unstructured":"Li D, Han M, Wang J (2012) Chaotic time series prediction based on a novel robust echo state network. IEEE Trans Neural Netw Learn Syst 23(5):787\u2013797. https:\/\/doi.org\/10.1109\/TNNLS.2012.2188414","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4948_CR23","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/J.NEUCOM.2011.11.021","volume":"82","author":"C Sheng","year":"2012","unstructured":"Sheng C, Zhao J, Liu Y, Wang W (2012) Prediction for noisy nonlinear time series by echo state network based on dual estimation. Neurocomputing 82:186\u2013195. https:\/\/doi.org\/10.1016\/J.NEUCOM.2011.11.021","journal-title":"Neurocomputing"},{"key":"4948_CR24","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/J.KNOSYS.2017.05.022","volume":"130","author":"X Sun","year":"2017","unstructured":"Sun X, Li T, Li Q, Huang Y, Li Y (2017) Deep belief echo-state network and its application to time series prediction. Knowl-Based Syst 130:17\u201329. https:\/\/doi.org\/10.1016\/J.KNOSYS.2017.05.022","journal-title":"Knowl-Based Syst"},{"key":"4948_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2008.09.049","author":"X Lin","year":"2009","unstructured":"Lin X, Yang Z, Song Y (2009) Short-term stock price prediction based on echo state networks. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2008.09.049","journal-title":"Expert Syst Appl"},{"key":"4948_CR26","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 (2017) Deep reservoir computing: a critical experimental analysis. Neurocomputing 268:87\u201399. https:\/\/doi.org\/10.1016\/j.neucom.2016.12.089","journal-title":"Neurocomputing"},{"key":"4948_CR27","unstructured":"Gallicchio C, Micheli A (2017) Deep Echo State Network (DeepESN): a brief survey, pp 1\u201313. Retrieved from http:\/\/arxiv.org\/abs\/1712.04323"},{"key":"4948_CR28","unstructured":"Ma Q, Shen L, Cottrell GW (2017) Deep-ESN: a multiple projection-encoding hierarchical reservoir computing framework. 14(8), 1\u201315. Retrieved from http:\/\/arxiv.org\/abs\/1711.05255"},{"key":"4948_CR29","unstructured":"Yahoo Finance S&P 500 data. https:\/\/finance.yahoo.com\/quote\/%5EGSPC\/history\/"},{"key":"4948_CR30","doi-asserted-by":"crossref","unstructured":"Stolfi DH, Alba E, Yao X (2017) Predicting car park occupancy rates in Smart Cities. In: Smart Cities: second international conference, Smart-CT 2017, Spain, 14\u201316 June 2017, pp 107\u2013117","DOI":"10.1007\/978-3-319-59513-9_11"},{"key":"4948_CR31","unstructured":"Weeks E (2015) Chaotic time series analysis. Department of Physics. Emory University. http:\/\/www.physics.emory.edu\/faculty\/weeks\/research\/tseries1.html"},{"key":"4948_CR32","unstructured":"The Pennsylvania State Climatologist. http:\/\/www.climate.psu.edu\/"},{"key":"4948_CR33","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s13748-013-0040-3","volume":"2","author":"H Fanaee-T","year":"2015","unstructured":"Fanaee-T H, Gama J (2015) Event labeling combining ensemble detectors and background knowledge. Prog Artifi Intell 2:113\u2013127","journal-title":"Prog Artifi Intell"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04948-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-04948-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04948-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,29]],"date-time":"2021-04-29T20:12:21Z","timestamp":1619727141000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-04948-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,30]]},"references-count":33,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["4948"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-04948-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,30]]},"assertion":[{"value":"29 October 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}