 Multivariate polynomial regression for identification of chaotic time seriesD. A. Vaccari and H. -K. Wang Mathematical and Computer Modelling of Dynamical Systems, 2007, vol. 13, issue 4, 395-412 Abstract: Multivariate polynomial regression was used to generate polynomial iterators for time series exhibiting autocorrelations. A stepwise technique was used to add and remove polynomial terms to ensure the model contained only those terms that produce a statistically significant contribution to the fit. An approach is described in which datasets are divided into three subsets for identification, estimation, and validation. This produces a parsimonious global model that is can greatly reduce the tendency towards undesirable behaviours such as overfitting or instability. The technique was found to be able to identify the nonlinear dynamic behaviour of simulated time series, as reflected in the geometry of the attractor and calculation of multiple Lyapunov exponents, even in noisy systems. The technique was applied to times series data obtained from simulations of the Lorenz and Mackey -- Glass equations with and without measurement noise. The model was also used to determine the embedding dimension of the Mackey -- Glass equation. Date: 2007 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:nmcmxx:v:13:y:2007:i:4:p:395-412 Ordering information: This journal article can be ordered from DOI: 10.1080/13873950600883691 Access Statistics for this article Mathematical and Computer Modelling of Dynamical Systems is currently edited by I. Troch More articles in Mathematical and Computer Modelling of Dynamical Systems from Taylor & Francis Journals |
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