In mathematics, a delay embedding theorem gives the conditions under which a chaotic dynamical system can be reconstructed from a sequence of observations of the state of a dynamical system. The reconstruction preserves the properties of the dynamical system that do not change under smooth coordinate changes, but it does not preserve the geometric shape of structures in phase space.
Takens's theorem is the 1981 delay embedding theorem of Floris Takens. It provides the conditions under which a smooth attractor can be reconstructed from the observations made with a generic function. Later results replaced the smooth attractor with a set of arbitrary box counting dimension and the class of generic functions with other classes of functions.
A delay embedding theorem uses an observation function to construct the embedding function. An observation function α must be twice-differentiable and associate a real number to any point of the attractor A. It must also be typical, so its derivative is of full rank and has no special symmetries in its components. The delay embedding theorem states that the function
is an embedding of the strange attractor A.
Simplified, slightly inaccurate version
Suppose the d-dimensional state vector xt evolves according to an unknown but continuous and (crucially) deterministic dynamic. Suppose, too, that the one-dimensional observable y is a smooth function of x, and “coupled” to all the components of x. Now at any time we can look not just at the present measurement y(t), but also at observations made at times removed from us by multiples of some lag , etc. If we use k lags, we have a k-dimensional vector. One might expect that, as the number of lags is increased, the motion in the lagged space will become more and more predictable, and perhaps in the limit would become deterministic. In fact, the dynamics of the lagged vectors become deterministic at a finite dimension; not only that, but the deterministic dynamics are completely equivalent to those of the original state space! (More exactly, they are related by a smooth, invertible change of coordinates, or diffeomorphism.) The magic embedding dimension k is at most 2d + 1, and often less.
Source: Shalizi, Cosma R. (2006).
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- N. Packard, J. Crutchfield, D. Farmer and R. Shaw (1980). "Geometry from a time series". Physical Review Letters 45 (9): 712–716. Bibcode:1980PhRvL..45..712P. doi:10.1103/PhysRevLett.45.712.
- F. Takens (1981). D. A. Rand and L.-S. Young, ed. "Dynamical Systems and Turbulence, Lecture Notes in Mathematics, vol. 898". Springer-Verlag. pp. 366–381.
- R. Mañé (1981). D. A. Rand and L.-S. Young, ed. "Dynamical Systems and Turbulence, Lecture Notes in Mathematics, vol. 898". Springer-Verlag. pp. 230–242.
- G. Sugihara and R.M. May (1990). "Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series". Nature 344 (6268): 734–741. Bibcode:1990Natur.344..734S. doi:10.1038/344734a0. PMID 2330029.
- Tim Sauer, James A. Yorke, and Martin Casdagli (1991). "Embedology". Journal of Statistical Physics 65 (3–4): 579–616. Bibcode:1991JSP....65..579S. doi:10.1007/BF01053745.
- G. Sugihara (1994). "Nonlinear forecasting for the classification of natural time series". Phil. Trans. R. Soc. Lond. A 348 (1688): 477–495. Bibcode:1994RSPTA.348..477S. doi:10.1098/rsta.1994.0106.
- P.A. Dixon, M.J. Milicich, and G. Sugihara (1999). "Episodic fluctuations in larval supply". Science 283 (5407): 1528–1530. Bibcode:1999Sci...283.1528D. doi:10.1126/science.283.5407.1528. PMID 10066174.
- G. Sugihara, M. Casdagli, E. Habjan, D. Hess, P. Dixon and G. Holland (1999). "Residual delay maps unveil global patterns of atmospheric nonlinearity and produce improved local forecasts". PNAS 96 (25): 210–215. Bibcode:1999PNAS...9614210S. doi:10.1073/pnas.96.25.14210. PMC 24416. PMID 10588685.
- C. Hsieh; Glaser, SM; Lucas, AJ; Sugihara, G (2005). "Distinguishing random environmental fluctuations from ecological catastrophes for the North Pacific Ocean". Nature 435 (7040): 336–340. Bibcode:2005Natur.435..336H. doi:10.1038/nature03553. PMID 15902256.