Chaotic time series Part I: Estimation of invariant properies in state space

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dc.creatorKugiumtzis, Dimitris
dc.creatorLillekjendlie, Bjoern
dc.creatorChristophersen, Nils
dc.date1994-01-14
dc.date1994-01-23
dc.date.accessioned2026-07-07T08:58:30Z
dc.date.available2026-07-07T08:58:30Z
dc.descriptionCertain deterministic non-linear systems may show chaotic behaviour. Time series derived from such systems seem stochastic when analyzed with linear techniques. However, uncovering the deterministic structure is important because it allows for construction of more realistic and better models and thus improved predictive capabilities. This paper describes key features of chaotic systems including strange attractors and Lyapunov exponents. The emphasis is on state space reconstruction techniques that are used to estimate these properties, given scalar observations. Data generated from equations known to display chaotic behaviour are used for illustration. A compilation of applications to real data from widely different fields is given. If chaos is found to be present, one may proceed to build non-linear models, which is the topic of the second paper in this series.
dc.description17 pages and 8 pages with figures all in uuencoded tar-compressed postscript format. Sent to
dc.identifierhttps://arxiv.org/abs/chao-dyn/9401004
dc.identifierhttp://arxiv.org/abs/chao-dyn/9401004
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/147351
dc.subjectChaotic Dynamics
dc.subjectCellular Automata and Lattice Gases
dc.titleChaotic time series Part I: Estimation of invariant properies in state space
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