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' 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.
Delay embedding theorems are simpler to state for discretetime dynamical systems. The state space of the dynamical system is a νdimensional manifold M. The dynamics is given by a smooth map

f: M \to M.
Assume that the dynamics f has a strange attractor A with box counting dimension d_{A}. Using ideas from Whitney's embedding theorem, A can be embedded in kdimensional Euclidean space with

k > 2 d_A.\
That is, there is a diffeomorphism φ that maps A into R^{k} such that the derivative of φ has full rank.
A delay embedding theorem uses an observation function to construct the embedding function. An observation function α must be twicedifferentiable 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

\phi_T(x)=\left(\alpha(x), \alpha\left(f(x)\right), \dots, \alpha\left(f^{k1}(x)\right)\right)
is an embedding of the strange attractor A.
Contents

Simplified, slightly inaccurate version 1

References 2

Further reading 3

External links 4
Simplified, slightly inaccurate version
Suppose the ddimensional state vector x_{t} evolves according to an unknown but continuous and (crucially) deterministic dynamic. Suppose, too, that the onedimensional 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 \tau: y_{t\tau}, y_{t2\tau} , etc. If we use k lags, we have a kdimensional 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 k \to \infty 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.^{[1]}
References

^ Shalizi, Cosma R. (2006). "Methods and Techniques of Complex Systems Science: An Overview". In Deisboeck, ThomasS; Kresh, J.Yasha. Complex Systems Science in Biomedicine. Springer US. pp. 33–114.
Further reading
External links

Attractor Reconstruction (scholarpedia)

[1] Scientio's ChaosKit product uses embedding to create analyses and predictions. Access is provided online via a web service and graphic interface.
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