Information theoretic test for nonlinearity in time series

@article{Palu1993InformationTT,
  title={Information theoretic test for nonlinearity in time series},
  author={Milan Palu{\vs} and V. Albrecht and Ivan Dvor̂ak},
  journal={Physics Letters A},
  year={1993},
  volume={175},
  pages={203-209}
}

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References

SHOWING 1-10 OF 53 REFERENCES
Direct test for determinism in a time series.
TLDR
A direct test for deterministic dynamics can be established by measurement of average directional vectors in a coarse-grained d-dimensional embedding of a time series, and examples are given to show the clear differences between deterministic and stochastic dynamics.
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
Information and entropy in strange attractors
A technique for analyzing time-series data from experiments is presented that provides estimates of four basic characteristics of a system: (1) the measure-theoretic entropy; (2) the accuracy of the
Characterization of Strange Attractors
A new measure of strange attractors is introduced which offers a practical algorithm to determine their character from the time series of a single observable. The relation of this new measure to
Deterministic nonperiodic flow
Finite systems of deterministic ordinary nonlinear differential equations may be designed to represent forced dissipative hydrodynamic flow. Solutions of these equations can be identified with
...
...