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Smoothness, degrees of freedom and Liapunov exponents of a time series

dc.contributor.authorMera Rivas, María Eugenia
dc.contributor.authorMorán Cabré, Manuel
dc.date.accessioned2023-06-21T01:36:22Z
dc.date.available2023-06-21T01:36:22Z
dc.date.issued2000
dc.description.abstractWe propose a set of tests addressing the issue of determining whether the generating law of a time series is a stochastic process or a chaotic dynamics. In the latter case, we test the smoothness and find the number of degrees of freedom of the underlying dynamics. We propose an adaptation of Eckmann and Ruelle algorithm for the computation of the Liapunov exponents of a time series. This algorithm computes efficiently the whole Liapunov spectrum of the observed dynamics, avoiding the problem of the spurious exponents.
dc.description.departmentDecanato
dc.description.facultyFac. de Ciencias Económicas y Empresariales
dc.description.refereedTRUE
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/27273
dc.identifier.issn2255-5471
dc.identifier.relatedurlhttps://economicasyempresariales.ucm.es/working-papers-ccee
dc.identifier.urihttps://hdl.handle.net/20.500.14352/64136
dc.issue.number09
dc.language.isoeng
dc.page.total59
dc.publication.placeMadrid
dc.publisherFacultad de Ciencias Económicas y Empresariales. Decanato
dc.relation.ispartofseriesDocumentos de Trabajo de la Facultad de Ciencias Económicas y Empresariales
dc.rightsAtribución-NoComercial-CompartirIgual 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/3.0/es/
dc.subject.keywordProcesos estocásticos
dc.subject.keywordExponente de Lyapunov.
dc.subject.ucmProcesos estocásticos
dc.subject.unesco1208.08 Procesos Estocásticos
dc.titleSmoothness, degrees of freedom and Liapunov exponents of a time series
dc.typetechnical report
dc.volume.number2000
dcterms.referencesBroomhead, D.S. and G.P. King. Extracting Qualitative Dynamics from Experimental Data, Physica 20D, (1986), 217-236. Broomhead, D.S. and G.P. King. Phase Portraits from a Time Series: A Singular System Approach, Nuclear Physics B, 2, (1987), 379-390. Brown, R., P. Bryan and H. Abarbanel. Computing the Liapunov spectnun of a dynarnical systern from an observed tierne series. Physical Review A, 43, (1991), 2787-2806. Cawley, R. and Guan-Hsong Hsu. Local Geometric Projection Method for Noise Reduction in Chaotic Maps and Flows, Physical Review A, 46, 6, (1992), 3057-3082. Eckrnann, J.P. and D. Ruelle. Ergodic Theory of Chaos and Strange Attractors, Reviews of Modern Physics, 57, 3, (1985), 617-656. Eckmann, J.P., S.O. Kamphorst, D. Ruelle and S. Ciliberto. Liapunov Exponents from Time Series, Physical Review A, 34, 6, (1986), 4971-4979. Gill, P.E., W. Murray and M.H. Wright. Numerical Linear Algebra and Optimization. Volume 1. Addison-Wesley Publishing Cornpany (1991). Grassberger, P. An Optimized Box-Assisted Algorithm for Fractal Dimcnsions, Phys. Lett. A, 148, (1990), 63-68. Kshirsagar, A.M. Multivariate Analysis. Marcel Dekker. NY (1972). Mattila, P. Geometry of Sets and Measures in Euclidean Spaces. Fractals and Rectifiability. Cambridge University Press (1995). Mera, M.E. and M. Morán. Convergence of the Eckmann and Ruelle Algorithm for the Estimation of Liapunov Exponents, (forthcoming in Ergodie Theory and Dynamical Systems). Mera, M.E. and M. Morán. Lp(µ)-Estimation of Tangent Maps, Journal of Mathematical Analysis and Applications 235, (1999), 454-469. Sano, M. and Y. Sawada. Measurement of the Lyapunov Spectrurn from a Chaotic Time Series. Physycal Review Letters, 55, 10, (1985), 1082-1085. Sauer, T., York, J.A. and Casdagli, M. Embedology. Journal of Statistical Physics, 65, 3/4, (1992), 579-616. Takens, F. Detecting Strange Attractors in Turbulence, Dynamical Systems and Turbulence. Lectures Notes in Mathematics, 898, 396, (1981).
dspace.entity.typePublication
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relation.isAuthorOfPublication36e295dc-70b7-4ede-868c-a83357a04413
relation.isAuthorOfPublication.latestForDiscovery71245121-5334-43ae-92e3-eb84a42790e8

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