Convenience functions

We provide a few convenience functions for widely used names for entropy or "entropy-like" quantities. Other arbitrary specialized convenience functions can easily be defined in a couple lines of code.

ComplexityMeasures.entropy_permutation — Function

entropy_permutation(x; τ = 1, m = 3, base = 2)

Compute the permutation entropy of x of order m with delay/lag τ. This function is just a convenience call to:

est = SymbolicPermutation(; m, τ)
entropy(Shannon(base), x, est)

See SymbolicPermutation for more info. Similarly, one can use SymbolicWeightedPermutation or SymbolicAmplitudeAwarePermutation for the weighted/amplitude-aware versions.

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ComplexityMeasures.entropy_wavelet — Function

entropy_wavelet(x; wavelet = Wavelets.WT.Daubechies{12}(), base = 2)

Compute the wavelet entropy. This function is just a convenience call to:

est = WaveletOverlap(wavelet)
entropy(Shannon(base), est, x)

See WaveletOverlap for more info.

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ComplexityMeasures.entropy_dispersion — Function

entropy_dispersion(x; base = 2, kwargs...)

Compute the dispersion entropy. This function is just a convenience call to:

est = Dispersion(kwargs...)
entropy(Shannon(base), est, x)

See Dispersion for more info.

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ComplexityMeasures.entropy_approx — Function

entropy_approx(x; m = 2, τ = 1, r = 0.2 * Statistics.std(x), base = MathConstants.e)

Convenience syntax for computing the approximate entropy (Pincus, 1991) for timeseries x.

This is just a wrapper for complexity(ApproximateEntropy(; m, τ, r, base), x) (see also ApproximateEntropy).

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ComplexityMeasures.entropy_sample — Function

entropy_sample(x; r = 0.2std(x), m = 2, τ = 1, normalize = true)

Convenience syntax for estimating the (normalized) sample entropy (Richman & Moorman, 2000) of timeseries x.

This is just a wrapper for complexity(SampleEntropy(; r, m, τ, base), x).

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