# Delay Coordinates Embedding

A timeseries recorded in some manner from a dynamical system can be used to gain information about the dynamics of the entire state space of the system. This can be done by constructing a new state space from the timeseries. One method that can do this is what is known as delay coordinates embedding or delay coordinates reconstruction.

## Timeseries embedding

Delay embeddings are done through embed:

DelayEmbeddings.embedFunction
embed(s, d, τ [, h])

Embed s using delay coordinates with embedding dimension d and delay time τ and return the result as a Dataset. Optionally use weight h, see below.

Here τ > 0, use genembed for a generalized version.

Description

If τ is an integer, then the $n$-th entry of the embedded space is

$$$(s(n), s(n+\tau), s(n+2\tau), \dots, s(n+(d-1)\tau))$$$

If instead τ is a vector of integers, so that length(τ) == d-1, then the $n$-th entry is

$$$(s(n), s(n+\tau), s(n+\tau), \dots, s(n+\tau[d-1]))$$$

The resulting set can have same invariant quantities (like e.g. lyapunov exponents) with the original system that the timeseries were recorded from, for proper d and τ. This is known as the Takens embedding theorem [Takens1981] [Sauer1991]. The case of different delay times allows embedding systems with many time scales, see[Judd1998].

If provided, h can be weights to multiply the entries of the embedded space. If h isa Real then the embedding is

$$$(s(n), h \cdot s(n+\tau), w^2 \cdot s(n+2\tau), \dots,w^{d-1} \cdot s(n+γ\tau))$$$

Otherwise h can be a vector of length d-1, which the decides the weights of each entry directly.

References

[Takens1981] : F. Takens, Detecting Strange Attractors in Turbulence — Dynamical Systems and Turbulence, Lecture Notes in Mathematics 366, Springer (1981)

[Sauer1991] : T. Sauer et al., J. Stat. Phys. 65, pp 579 (1991)

Here are some examples of embedding a 3D continuous chaotic system:

using DynamicalSystems, PyPlot

ds = Systems.gissinger(ones(3))
data = trajectory(ds, 1000.0, dt = 0.05)

xyz = columns(data)

figure(figsize = (12,10))
k = 1
for i in 1:3
for τ in [5, 30, 100]
R = embed(xyz[i], 2, τ)
ax = subplot(3,3,k)
plot(R[:, 1], R[:, 2], color = "C$(k-1)", lw = 0.8) title("var =$i, τ = $τ") global k+=1 end end tight_layout() suptitle("2D reconstructed space") subplots_adjust(top=0.9) τ and dt Keep in mind that whether a value of τ is "reasonable" for continuous systems depends on dt. In the above example the value τ=30 is good, only for the case of using dt = 0.05. For shorter/longer dt one has to adjust properly τ so that their product τ*dt is the same. ### Embedding Functors The high level function embed utilize a low-level interface for creating embedded vectors on-the-fly. The high level interface simply loops over the low level interface. The low level interface is composed of the following two structures: DelayEmbeddings.DelayEmbeddingType DelayEmbedding(γ, τ, h = nothing) → embedding Return a delay coordinates embedding structure to be used as a function-like-object, given a timeseries and some index. Calling embedding(s, n) will create the n-th delay vector of the embedded space, which has γ temporal neighbors with delay(s) τ. γ is the embedding dimension minus 1, τ is the delay time(s) while h are extra weights, as in embed for more. Be very careful when choosing n, because @inbounds is used internally. Use τrange! DelayEmbeddings.τrangeFunction τrange(s, de::AbstractEmbedding) Return the range r of valid indices n to create delay vectors out of s using de. ## Generalized embeddings DelayEmbeddings.genembedFunction genembed(s, τs, js = ones(...); ws = nothing) → dataset Create a generalized embedding of s which can be a timeseries or arbitrary Dataset, and return the result as a new Dataset. The generalized embedding works as follows: • τs denotes what delay times will be used for each of the entries of the delay vector. It is recommended that τs = 0. τs is allowed to have negative entries as well. • js denotes which of the timeseries contained in s will be used for the entries of the delay vector. js can contain duplicate indices. • ws are optional weights that weight each embedded entry (the i-th entry of the delay vector is weighted by ws[i]). If provided, it is recommended that ws = 1 τs, js, ws are tuples (or vectors) of length D, which also coincides with the embedding dimension. For example, imagine input trajectory$s = [x, y, z]$where$x, y, z$are timeseries (the columns of the Dataset). If js = (1, 3, 2) and τs = (0, 2, -7) the created delay vector at each step$n\$ will be

$$$(x(n), z(n+2), y(n-7))$$$

Using ws = (1, 0.5, 0.25) as well would create

$$$(x(n), \frac{1}{2} z(n+2), \frac{1}{4} y(n-7))$$$

js can be skipped, defaulting to index 1 (first timeseries) for all delay entries, while it has no effect if s is a timeseries instead of a Dataset.

See also embed. Internally uses GeneralizedEmbedding.

DelayEmbeddings.GeneralizedEmbeddingType
GeneralizedEmbedding(τs, js = ones(length(τs)), ws = nothing) -> embedding

Return a delay coordinates embedding structure to be used as a functor. Given a timeseries or trajectory (i.e. Dataset) s and calling

embedding(s, n)

will create the delay vector of the n-th point of s in the embedded space using generalized embedding (see genembed).

js is ignored for timeseries input s (since all entries of js must be 1 in this case) and in addition js defaults to (1, ..., 1) for all τ.

Be very careful when choosing n, because @inbounds is used internally. Use τrange!