Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series.
An approach is presented for making short-term predictions about the trajectories of chaotic dynamical systems. The method is applied to data on measles, chickenpox, and marine phytoplankton populations, to show how apparent noise associated with deterministic chaos can be distinguished from sampling error and other sources of externally induced environmental noise.
Sugihara
G
G
Scripps Institution of Oceanography, University of California, San Diego, 92093.
May
R M
RM
D000818:Animals; D002644:Chickenpox / Q000453:epidemiology; D005544:Forecasting; D006801:Humans; D008433:Mathematics; D008457:Measles / Q000453:epidemiology; D009519:New York City; D010839:Phytoplankton; D011157:Population Dynamics; D013997:Time Factors