Complexity measures

complexity(c::ComplexityEstimator, x) → m::Real

Estimate a complexity measure according to c for input datax, where c is an instance of any subtype of ComplexityEstimator:

• ApproximateEntropy.
• LempelZiv76.
• MissingDispersionPatterns.
• ReverseDispersion.
• SampleEntropy.
• BubbleEntropy.
• StatisticalComplexity.

complexity_normalized(c::ComplexityEstimator, x) → m::Real ∈ [a, b]

The same as complexity, but the result is normalized to the interval [a, b], where [a, b] depends on c.

ComplexityEstimator

Supertype for estimators for various complexity measures that are not entropies in the strict mathematical sense.

See complexity for all available estimators.

ApproximateEntropy <: ComplexityEstimator
ApproximateEntropy([x]; r = 0.2std(x), kwargs...)

An estimator for the approximate entropy (Pincus, 1991) complexity measure, used with complexity.

The keyword argument r is mandatory if an input timeseries x is not provided.

Keyword arguments

• r::Real: The radius used when querying for nearest neighbors around points. Its value should be determined from the input data, for example as some proportion of the standard deviation of the data.
• m::Int = 2: The embedding dimension.
• τ::Int = 1: The embedding lag.
• base::Real = MathConstants.e: The base to use for the logarithm. Pincus (1991) uses the natural logarithm.

Description

Approximate entropy (ApEn) is defined as

\[ApEn(m ,r) = \lim_{N \to \infty} \left[ \phi(x, m, r) - \phi(x, m + 1, r) \right].\]

Approximate entropy is estimated for a timeseries x, by first embedding x using embedding dimension m and embedding lag τ, then searching for similar vectors within tolerance radius r, using the estimator described below, with logarithms to the given base (natural logarithm is used in Pincus, 1991).

Specifically, for a finite-length timeseries x, an estimator for $ApEn(m ,r)$ is

\[ApEn(m, r, N) = \phi(x, m, r, N) - \phi(x, m + 1, r, N),\]

where N = length(x) and

\[\phi(x, k, r, N) = \dfrac{1}{N-(k-1)\tau} \sum_{i=1}^{N - (k-1)\tau} \log{\left( \sum_{j = 1}^{N-(k-1)\tau} \dfrac{\theta(d({\bf x}_i^m, {\bf x}_j^m) \leq r)}{N-(k-1)\tau} \right)}.\]

Here, $\theta(\cdot)$ returns 1 if the argument is true and 0 otherwise, $d({\bf x}_i, {\bf x}_j)$ returns the Chebyshev distance between vectors ${\bf x}_i$ and ${\bf x}_j$, and the k-dimensional embedding vectors are constructed from the input timeseries $x(t)$ as

\[{\bf x}_i^k = (x(i), x(i+τ), x(i+2τ), \ldots, x(i+(k-1)\tau)).\]

SampleEntropy([x]; r = 0.2std(x), kwargs...) <: ComplexityEstimator

An estimator for the sample entropy complexity measure (Richman and Moorman, 2000), used with complexity and complexity_normalized.

The keyword argument r is mandatory if an input timeseries x is not provided.

Keyword arguments

• r::Real: The radius used when querying for nearest neighbors around points. Its value should be determined from the input data, for example as some proportion of the standard deviation of the data.
• m::Int = 2: The embedding dimension.
• τ::Int = 1: The embedding lag.

Description

An estimator for sample entropy using radius r, embedding dimension m, and embedding lag τ is

\[SampEn(m,r, N) = -\ln{\dfrac{A(r, N)}{B(r, N)}}.\]

Here,

\[\begin{aligned} B(r, m, N) = \sum_{i = 1}^{N-m\tau} \sum_{j = 1, j \neq i}^{N-m\tau} \theta(d({\bf x}_i^m, {\bf x}_j^m) \leq r) \\ A(r, m, N) = \sum_{i = 1}^{N-m\tau} \sum_{j = 1, j \neq i}^{N-m\tau} \theta(d({\bf x}_i^{m+1}, {\bf x}_j^{m+1}) \leq r) \\ \end{aligned},\]

where $\theta(\cdot)$ returns 1 if the argument is true and 0 otherwise, and $d(x, y)$ computes the Chebyshev distance between $x$ and $y$, and ${\bf x}_i^{m}$ and ${\bf x}_i^{m+1}$ are m-dimensional and m+1-dimensional embedding vectors, where k-dimensional embedding vectors are constructed from the input timeseries $x(t)$ as

\[{\bf x}_i^k = (x(i), x(i+τ), x(i+2τ), \ldots, x(i+(k-1)\tau)).\]

Quoting Richman & Moorman (2002): "SampEn(m,r,N) will be defined except when B = 0, in which case no regularity has been detected, or when A = 0, which corresponds to a conditional probability of 0 and an infinite value of SampEn(m,r,N)". In these cases, NaN is returned.

If computing the normalized measure, then the resulting sample entropy is on [0, 1].

See also: entropy_sample.

MissingDispersionPatterns <: ComplexityEstimator
MissingDispersionPatterns(o = Dispersion()) → mdp

An estimator for the number of missing dispersion patterns (MDP), a complexity measure which can be used to detect nonlinearity in time series (Zhou et al., 2023).

Used with complexity or complexity_normalized.

Description

When used with complexity, complexity(mdp) is syntactically equivalent with just missing_outcomes(o). When used with complexity_normalized, the normalization is simply missing_outcomes(o)/total_outcomes(o).

Usage

In Zhou et al. (2023), MissingDispersionPatterns is used to detect nonlinearity in time series by comparing the MDP for a time series x to values for an ensemble of surrogates of x, as per the standard analysis of TimeseriesSurrogates.jl If the MDP value of $x$ is significantly larger than some high quantile of the surrogate distribution, then it is taken as evidence for nonlinearity.

See also: Dispersion, ReverseDispersion, total_outcomes.

ReverseDispersion <: ComplexityEstimator
ReverseDispersion(; c = 3, m = 2, τ = 1, check_unique = true)

Estimator for the reverse dispersion entropy complexity measure (Li et al., 2019).

Description

Li et al. (2019) defines the reverse dispersion entropy as

\[H_{rde} = \sum_{i = 1}^{c^m} \left(p_i - \dfrac{1}{{c^m}} \right)^2 = \left( \sum_{i=1}^{c^m} p_i^2 \right) - \dfrac{1}{c^{m}}\]

where the probabilities $p_i$ are obtained precisely as for the Dispersion probability estimator. Relative frequencies of dispersion patterns are computed using the given encoding scheme , which defaults to encoding using the normal cumulative distribution function (NCDF), as implemented by GaussianCDFEncoding, using embedding dimension m and embedding delay τ. Recommended parameter values(Li et al., 2019) are m ∈ [2, 3], τ = 1 for the embedding, and c ∈ [3, 4, …, 8] categories for the Gaussian mapping.

If normalizing, then the reverse dispersion entropy is normalized to [0, 1].

The minimum value of $H_{rde}$ is zero and occurs precisely when the dispersion pattern distribution is flat, which occurs when all $p_i$s are equal to $1/c^m$. Because $H_{rde} \geq 0$, $H_{rde}$ can therefore be said to be a measure of how far the dispersion pattern probability distribution is from white noise.

Data requirements

The input must have more than one unique element for the default GaussianCDFEncoding to be well-defined. Li et al. (2019) recommends that x has at least 1000 data points.

If check_unique == true (default), then it is checked that the input has more than one unique value. If check_unique == false and the input only has one unique element, then a InexactError is thrown when trying to compute probabilities.

StatisticalComplexity <: ComplexityEstimator
StatisticalComplexity(; kwargs...)

An estimator for the statistical complexity and entropy, originally by (Rosso et al., 2007) and generalized by Rosso et al. (2013).

Our implementation extends the generalization to any valid distance metric, any OutcomeSpace with a priori known total_outcomes, any ProbabilitiesEstimator, and any normalizable discrete InformationMeasure.

Used with complexity.

Keyword arguments

• o::OutcomeSpace = OrdinalPatterns{3}(). The OutcomeSpace, which controls how the input data are discretized.
• pest::ProbabilitiesEstimator = RelativeAmount(): The ProbabilitiesEstimator used to estimate probabilities over the discretized input data.
• hest = Renyi(): An InformationMeasure of choice. Any information measure that defines information_maximum is valid here. The measure will be estimated using the PlugIn estimator. For example, you can use hest = Renyi(). Typically, for the information measure, an entropy such as Shannon or Renyi is used. However, other measures can be used too, for example extropies like ShannonExtropy (which were not treated in Rosso et al. (2013)).
• dist<:SemiMetric = JSDivergence(): The distance measure (from Distances.jl) to use for estimating the distance between the estimated probability distribution and a uniform distribution with the same maximal number of outcomes.

Description

Statistical complexity is defined as

\[C_q[P] = \mathcal{H}_q\cdot \mathcal{Q}_q[P],\]

where $Q_q$ is a "disequilibrium" obtained from a distance-measure and $H_q$ a disorder measure. In the original paper(Rosso et al., 2007), this complexity measure was defined via an ordinal pattern-based probability distribution (see OrdinalPatterns), using Shannon entropy as the information measure, and the Jensen-Shannon divergence as a distance measure.

Our implementation is a further generalization of the complexity measure developed in Rosso et al. (2013). We let $H_q$be any normalizable InformationMeasure, e.g. Shannon, Renyi or Tsallis entropy, and we let $Q_q$ be either on the Euclidean, Wooters, Kullback, q-Kullback, Jensen or q-Jensen distance as

\[Q_q[P] = Q_q^0\cdot D[P, P_e],\]

where $D[P, P_e]$ is the distance between the obtained distribution $P$ and a uniform distribution with the same maximum number of bins, measured by the distance measure dist.

Usage

The statistical complexity is exclusively used in combination with the chosen information measure (typically an entropy). The estimated value of the information measure can be accessed as a Ref value of the struct as

x = randn(100)
c = StatisticalComplexity()
compl = complexity(c, x)
entr = c.entr_val[]

complexity(c::StatisticalComplexity, x) returns only the statistical complexity. To obtain both the value of the entropy (or other information measure) and the statistical complexity together as a Tuple, use the wrapper entropy_complexity.

See also: entropy_complexity_curves.

entropy_complexity(c::StatisticalComplexity, x) → (h, compl)

Return a information measure h and the corresponding StatisticalComplexity value compl.

Useful when wanting to plot data on the "entropy-complexity plane". See also entropy_complexity_curves.

entropy_complexity_curves(c::StatisticalComplexity; 
    num_max=1, num_min=1000) -> (min_entropy_complexity, max_entropy_complexity)

Calculate the maximum complexity-entropy curve for the statistical complexity according to Rosso et al. (2007) for num_max * total_outcomes(c.o) different values of the normalized information measure of choice (in case of the maximum complexity curves) and num_min different values of the normalized information measure of choice (in case of the minimum complexity curve).

This function can also be used to compute the maximum "complexity-extropy curve" if c.hest is e.g. ShannonExtropy, which is the equivalent of the complexity-entropy curves, but using extropy instead of entropy.

Description

The way the statistical complexity is designed, there is a minimum and maximum possible complexity for data with a given value of an information measure. The calculation time of the maximum complexity curve grows as O(total_outcomes(c.o)^2), and thus takes very long for high numbers of outcomes. This function is inspired by S. Sippels implementation in statcomp (Sippel et al., 2016).

This function will work with any ProbabilitiesEstimator where total_outcomes is known a priori.

LempelZiv76 <: ComplexityEstimator
LempelZiv76()

The Lempel-Ziv, or LempelZiv76, complexity measure (Lempel and Ziv, 1976), which is used with complexity and complexity_normalized.

For results to be comparable across sequences with different length, use the normalized version. Normalized LempelZiv76-complexity is implemented as given in Amigó et al. (2004). The normalized measure is close to zero for very regular signals, while for random sequences, it is close to 1 with high probability^[Amigó2004]. Note: the normalized LempelZiv76 complexity can be higher than 1^[Amigó2004].

The LempelZiv76 measure applies only to binary sequences, i.e. sequences with a two-element alphabet (precisely two distinct outcomes). For performance optimization, we do not check the number of unique elements in the input. If your input sequence is not binary, you must encode it first using one of the implemented Encoding schemes (or encode your data manually).

BubbleEntropy <: ComplexityEstimator
BubbleEntropy(; m = 3, τ = 1, definition = Renyi(q = 2))

The BubbleEntropy complexity estimator (Manis et al., 2017) is just a difference between two entropies, each computed with the BubbleSortSwaps outcome space, for embedding dimensions m + 1 and m, respectively.

Manis et al. (2017) use the Renyi entropy of order q = 2 as the information measure definition, but here you can use any InformationMeasure. Manis et al. (2017) formulates the "bubble entropy" as the normalized measure below, while here you can also compute the unnormalized measure.

Definition

For input data x, the "bubble entropy" is computed by first embedding the input data using embedding dimension m and embedding delay τ (call the embedded pts y), and then computing the difference between the two entropies:

\[BubbleEn_T(τ) = H_T(y, m + 1) - H_T(y, m)\]

where $H_T(y, m)$ and $H_T(y, m + 1)$ are entropies of type $T$ (e.g. Renyi) computed with the input data x embedded to dimension $m$ and $m+1$, respectively. Use complexity to compute this non-normalized version. Use complexity_normalized to compute the normalized difference of entropies:

\[BubbleEn_H(τ)^{norm} = \dfrac{H_T(x, m + 1) - H_T(x, m)}{max(H_T(x, m + 1)) - max(H_T(x, m))},\]

where the maximum of the entropies for dimensions m and m + 1 are computed using information_maximum.

Example

using ComplexityMeasures
x = rand(1000)
est = BubbleEntropy(m = 5, τ = 3)
complexity(est, x)