Significant developments in the last decade have provided techniques that identify signals generated by essentially nonlinear and possibly chaotic dynamics. Nonlinear or even chaotic dynamics have been observed or suspected in time series generated by measurements of stars and galaxies, heart and brain activity, lasers and electrical circuits, plasmas and rocket exhaust, for example. The object of this presentation is the demonstration of the utility of the heretofore overlooked combination of surrogate data analysis with the ApEn statistic for the identification of essentially nonlinear time series. This new analysis has been applied to data from laser optics, fluid dynamics, astronomy, ecology, electrical engineering, and popular chaotic models.
*This work was carried out in collaboration with Drs. Daphne Stoner, Charles Tolle, Karen Leighly and Steven Pincus, and was supported by the Office of Environmental Management, Department of Energy, under DOE-ID Operations Office Contract DE-AC07-99ID13727