Detecting nonlinear structure in time series [electronic resource].
- Washington, D.C. : United States. Dept. of Energy, 1991.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
- Physical Description:
- Pages: (8 pages) : digital, PDF file
- Additional Creators:
- Los Alamos National Laboratory
United States. Department of Energy
United States. Department of Health and Human Services
United States. Department of Energy. Office of Scientific and Technical Information
- We describe an approach for evaluating the statistical significance of evidence for nonlinearity in a time series. The formal application of our method requires the careful statement of a null hypothesis which characterizes a candidate linear process, the generation of an ensemble of surrogate'' data sets which are similar to the original time series but consistent with the null hypothesis, and the computation of a discriminating statistic for the original and for each of the surrogate data sets. The idea is to test the original time series against the null hypothesis by checking whether the discriminating statistic computed for the original time series differs significantly from the statistics computed for each of the surrogate sets. While some data sets very cleanly exhibit low-dimensional chaos, there are many cases where the evidence is sketchy and difficult to evaluate. We hope to provide a framework within which such claims of nonlinearity can be evaluated. 5 refs., 4 figs.
- Published through SciTech Connect.
1. experimental chaos conference, Arlington, VA (United States), 1-3 Oct 1991.
- Funding Information:
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