{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T09:31:42Z","timestamp":1743067902086,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319051697"},{"type":"electronic","value":"9783319051703"}],"license":[{"start":{"date-parts":[[2014,1,1]],"date-time":"2014-01-01T00:00:00Z","timestamp":1388534400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2014,1,1]],"date-time":"2014-01-01T00:00:00Z","timestamp":1388534400000},"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":[[2014]]},"DOI":"10.1007\/978-3-319-05170-3_24","type":"book-chapter","created":{"date-parts":[[2014,3,26]],"date-time":"2014-03-26T13:53:08Z","timestamp":1395841988000},"page":"353-367","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Temporal Validated Meta-Learning for Long-Term Forecasting of Chaotic Time Series Using Monte Carlo Cross-Validation"],"prefix":"10.1007","author":[{"given":"Rigoberto","family":"Fonseca","sequence":"first","affiliation":[]},{"given":"Pilar","family":"G\u00f3mez-Gil","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2014,3,27]]},"reference":[{"key":"24_CR1","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1063\/1.881528","volume":"49","author":"HDI Abarbanel","year":"1996","unstructured":"Abarbanel, H.D.I., Gollub, J.P.: Analysis of observed chaotic data. Phys. Today 49, 86 (1996)","journal-title":"Phys. Today"},{"key":"24_CR2","volume-title":"Long-Range Forecasting from Crystal Ball to Computer","author":"JS Armstrong","year":"1985","unstructured":"Armstrong, J.S.: Long-Range Forecasting from Crystal Ball to Computer, 2nd edn. Wiley, New York (1985)","edition":"2"},{"key":"24_CR3","unstructured":"Beale, M.H., Hagan, M.T., Demuth, H.B.: Neural Network Toolbox User\u2019s Guide R2012b: MathWorks (2012)"},{"key":"24_CR4","unstructured":"Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: Time Series Analysis Forecasting and Control, 3rd edn. In: Jerome Grant, (ed.) Prentice-Hall International, Upper Saddle River (1994)"},{"key":"24_CR5","volume-title":"Time Series: Theory and Methods","author":"PJ Brockwell","year":"2006","unstructured":"Brockwell, P.J., Davis, R.A.: Time Series: Theory and Methods, 2nd edn. Springer, New York (2006)","edition":"2"},{"issue":"1\u20132","key":"24_CR6","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/S0167-2789(97)00118-8","volume":"110","author":"L Cao","year":"1997","unstructured":"Cao, L.: Practical method for determining the minimum embedding dimension of a scalar time series. Phys. D: Nonlinear Phenom. 110(1\u20132), 43\u201350 (1997)","journal-title":"Phys. D: Nonlinear Phenom."},{"key":"24_CR7","unstructured":"Crone, S.F.: Competition instructions (Online). http:\/\/www.neural-forecasting-competition.com\/instructions.htm (2008, Feb)"},{"issue":"3","key":"24_CR8","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1016\/j.ijforecast.2011.04.001","volume":"27","author":"SF Crone","year":"2011","unstructured":"Crone, S.F., Hibon, M., Nikolopoulos, K.: Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction. Int. J. Forecast. 27(3), 635\u2013660 (2011)","journal-title":"Int. J. Forecast."},{"issue":"3","key":"24_CR9","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1016\/j.ijforecast.2006.01.001","volume":"22","author":"JG De Gooijer","year":"2006","unstructured":"De Gooijer, J.G., Hyndman, R.J.: 25\u00a0years of time series forecasting. Int. J. Forecast. 22(3), 443\u2013473 (2006). Twenty five years of forecasting","journal-title":"Int. J. Forecast."},{"issue":"3","key":"24_CR10","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/j.advwatres.2010.01.001","volume":"33","author":"CT Dhanya","year":"2010","unstructured":"Dhanya, C.T., Nagesh Kumar, D.: Nonlinear ensemble prediction of chaotic daily rainfall. Adv. Water Res. 33(3), 327\u2013347 (2010)","journal-title":"Adv. Water Res."},{"issue":"3","key":"24_CR11","first-page":"182","volume":"3","author":"E Diaconescu","year":"2008","unstructured":"Diaconescu, E.: The use of NARX neural networks to predict chaotic time series. WSEAS Trans. Comp. Res. 3(3), 182\u2013191 (2008)","journal-title":"WSEAS Trans. Comp. Res."},{"key":"24_CR12","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1023\/B:MACH.0000015881.36452.6e","volume":"54","author":"S Dzeroski","year":"2004","unstructured":"Dzeroski, S., Zenko, B.: Is combining classifiers with stacking better than selecting the best one? Mach. Learn. 54, 255\u2013273 (2004)","journal-title":"Mach. Learn."},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Fonseca-Delgado, F., G\u00f3mez-Gil, P.: An assessment of ten-fold and Monte Carlo cross validations for time series forecasting. In: 10th International Conference on Electrical Engineering, Computing Science and Automatic Control. Mexico (2013)","DOI":"10.1109\/ICEEE.2013.6676075"},{"key":"24_CR14","unstructured":"Fonseca-Delgado, R., G\u00f3mez-Gil, P.: Temporal self-organized meta-learning for predicting chaotic time series. In: 5th Mexican Conference on Pattern Recognition. Queretaro (2013)"},{"key":"24_CR15","volume-title":"Neural Networks A Comprehensive Foundation","author":"S Haykin","year":"1999","unstructured":"Haykin, S.: Neural Networks A Comprehensive Foundation, 2nd edn. Pearson Prentice Hall, New York (1999)","edition":"2"},{"issue":"2","key":"24_CR16","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1063\/1.166424","volume":"9","author":"R Hegger","year":"1999","unstructured":"Hegger, R., Kantz, H., Schreiber, T.: Practical implementation of nonlinear time series methods: The TISEAN package. Chaos: Interdiscip. J. Nonlinear Sci. 9(2), 413\u2013435 (1999)","journal-title":"Chaos: Interdiscip. J. Nonlinear Sci."},{"key":"24_CR17","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511755798","volume-title":"Nonlinear Time Series Analysis","author":"H Kantz","year":"2003","unstructured":"Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis. Cambridge University Press, Cambridge (2003)"},{"key":"24_CR18","doi-asserted-by":"publisher","first-page":"3403","DOI":"10.1103\/PhysRevA.45.3403","volume":"45","author":"MB Kennel","year":"1992","unstructured":"Kennel, M.B., Brown, R., Abarbanel, H.D.I.: Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys. Rev. A 45, 3403\u20133411 (1992)","journal-title":"Phys. Rev. A"},{"issue":"1\u20132","key":"24_CR19","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","volume":"97","author":"R Kohavi","year":"1997","unstructured":"Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1\u20132), 273\u2013324 (1997). Relevance","journal-title":"Artif. Intell."},{"issue":"10\u201312","key":"24_CR20","doi-asserted-by":"publisher","first-page":"2006","DOI":"10.1016\/j.neucom.2009.09.020","volume":"73","author":"C Lemke","year":"2010","unstructured":"Lemke, C., Gabrys, B.: Meta-learning for time series forecasting and forecast combination. Neurocomputing 73(10\u201312), 2006\u20132016 (2010)","journal-title":"Neurocomputing"},{"issue":"2","key":"24_CR21","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1080\/0020718508961130","volume":"41","author":"IJ Leontaritis","year":"1985","unstructured":"Leontaritis, I.J., Billings, S.A.: Input-output parametric models for non-linear systems Part II: Stochastic non-linear systems. Int. J. Control 41(2), 329\u2013344 (1985)","journal-title":"Int. J. Control"},{"issue":"4300","key":"24_CR22","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"},{"issue":"4","key":"24_CR23","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1016\/S0169-2070(00)00057-1","volume":"16","author":"S Makridakis","year":"2000","unstructured":"Makridakis, S., Hibon, M.: The M3-competition: Results, conclusions and implications. Int. J. Forecast. 16(4), 451\u2013476 (2000). The M3-Competition","journal-title":"Int. J. Forecast."},{"issue":"11","key":"24_CR24","doi-asserted-by":"publisher","first-page":"4427","DOI":"10.1016\/j.eswa.2013.01.047","volume":"40","author":"M Matijas","year":"2013","unstructured":"Matijas, M., Suykens, J.A.K., Krajcar, S.: Load forecasting using a multivariate meta-learning system. Expert Syst. Appl. 40(11), 4427\u20134437 (2013)","journal-title":"Expert Syst. Appl."},{"issue":"387","key":"24_CR25","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1080\/01621459.1984.10478083","volume":"79","author":"RR Picard","year":"1984","unstructured":"Picard, R.R., Cook, R.D.: Cross-validation of regression models. J. Am. Stat. Assoc. 79(387), 575\u2013583 (1984)","journal-title":"J. Am. Stat. Assoc."},{"key":"24_CR26","unstructured":"Poincare, H.: Memoire sur les courbes definies par une equation differentielle. Resal J. 3, VII. 375\u2013422 (1881)"},{"issue":"1\u20132","key":"24_CR27","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/0167-2789(93)90009-P","volume":"65","author":"MT Rosenstein","year":"1993","unstructured":"Rosenstein, M.T., Collins, J.J., De Luca, C.J.: A practical method for calculating largest Lyapunov exponents from small data sets. Phys. D 65(1\u20132), 117\u2013134 (1993)","journal-title":"Phys. D"},{"issue":"3","key":"24_CR28","first-page":"78","volume":"6","author":"M Sandri","year":"1996","unstructured":"Sandri, M.: Numerical calculation of Lyapunov exponents. Math. J. 6(3), 78\u201384 (1996)","journal-title":"Math. J."},{"issue":"422","key":"24_CR29","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1080\/01621459.1993.10476299","volume":"88","author":"J Shao","year":"1993","unstructured":"Shao, J.: Linear model selection by cross-validation. J. Am. Stat. Assoc. 88(422), 486\u2013494 (1993)","journal-title":"J. Am. Stat. Assoc."},{"issue":"2","key":"24_CR30","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1109\/3477.558801","volume":"27","author":"HT Siegelmann","year":"1997","unstructured":"Siegelmann, H.T., Horne, B.G., Giles, C.L.: Computational capabilities of recurrent NARX neural networks. Syst. Man Cybern. B Cybern. IEEE Trans. 27(2), 208\u2013215 (1997)","journal-title":"Syst. Man Cybern. B Cybern. IEEE Trans."},{"issue":"8","key":"24_CR31","doi-asserted-by":"crossref","first-page":"7067","DOI":"10.1016\/j.eswa.2012.01.039","volume":"39","author":"S.B. Taieb","year":"2012","unstructured":"Taieb, S.B., Bontempi, G., Atiya, A.F., Sorjamaa, A.: A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. Expert Syst. Appl. 39(8), 7067\u20137083 (2012)","journal-title":"Expert Syst. Appl."},{"issue":"10\u201312","key":"24_CR32","doi-asserted-by":"publisher","first-page":"2581","DOI":"10.1016\/j.neucom.2008.10.017","volume":"72","author":"X Wang","year":"2009","unstructured":"Wang, X., Smith-Miles, K., Hyndman, R.: Rule induction for forecasting method selection: Meta-learning the characteristics of univariate time series. Neurocomputing 72(10\u201312), 2581\u20132594 (2009)","journal-title":"Neurocomputing"},{"issue":"2","key":"24_CR33","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","volume":"5","author":"DH Wolpert","year":"1992","unstructured":"Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241\u2013259 (1992)","journal-title":"Neural Netw."},{"issue":"2","key":"24_CR34","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1002\/cem.858","volume":"18","author":"QS Xu","year":"2004","unstructured":"Xu, Q.S., Liang, Y.Z., Du, Y.P.: Monte Carlo cross-validation for selecting a model and estimating the prediction error in multivariate calibration. J. Chemom. 18(2), 112\u2013120 (2004)","journal-title":"J. Chemom."},{"issue":"3","key":"24_CR35","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1023\/A:1011392725041","volume":"13","author":"Z.R. Yang","year":"2001","unstructured":"Yang, Z.R., Lu, W., Harrison, R.G.: Evolving stacked time series predictors with multiple window scales and sampling gaps. Neural Process. Lett. 13(3), 203\u2013211 (2001)","journal-title":"Neural Process. Lett."}],"container-title":["Studies in Computational Intelligence","Recent Advances on Hybrid Approaches for Designing Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-05170-3_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T22:26:16Z","timestamp":1675808776000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-05170-3_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014]]},"ISBN":["9783319051697","9783319051703"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-05170-3_24","relation":{},"ISSN":["1860-949X","1860-9503"],"issn-type":[{"type":"print","value":"1860-949X"},{"type":"electronic","value":"1860-9503"}],"subject":[],"published":{"date-parts":[[2014]]},"assertion":[{"value":"27 March 2014","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}