Statistical test for dynamical nonstationarity in observed time-series data

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Abstract
Information in the time distribution of points in a state space reconstructed from observed data yields a test for “nonstationarity.” Framed in terms of a statistical hypothesis test, this numerical algorithm can discern whether some underlying slow changes in parameters have taken place. The method examines a fundamental object in nonlinear dynamics, the geometry of orbits in state space, with corrections to overcome difficulties in real dynamical data which cause naive statistics to fail.

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