Detecting Determinism in Time Series Data: When Should We Bother to Build Models?
نویسندگان
چکیده
Nonlinear modeling routines are often applied in an effort to extract underlying determinism from time series data. The best of these methods perform well for short noisy time series when there is determinism in the underlying system. We show that nonlinear modeling does not distinguish between a static nonlinear transformation of linearly filtered noise and dynamic nonlinearity. To relieve this problem we recommend that surrogate data methods should be applied prior to nonlinear modeling, and the results of that analysis used to guide model selection.
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