Dispersion entropy
Entropies.dispersion_entropy
— Functiondispersion_entropy(x, s = GaussianSymbolization(n_categories = 5);
m = 3, τ = 1, q = 1, base = MathConstants.e)
Compute the (order-q
generalized) dispersion entropy to the given base
of the univariate time series x
. Relative frequencies of dispersion patterns are computed using the symbolization scheme s
with embedding dimension m
and embedding delay τ
.
Recommended parameter values[Li2018] are m ∈ [2, 3]
, τ = 1
, and n_categories ∈ [3, 4, …, 8]
for the Gaussian mapping (defaults to 5).
Description
Dispersion entropy characterizes the complexity and irregularity of a time series. This implementation follows the description in Li et al. (2018)[Li2018], which is based on Azami & Escudero (2018)[Azami2018], additionally allowing the computation of generalized dispersion entropy of order q
(default is q = 1
, which is the Shannon entropy).
Data requirements
Li et al. (2018) recommends that x
has at least 1000 data points.
- Li2018Li, G., Guan, Q., & Yang, H. (2018). Noise reduction method of underwater acoustic signals based on CEEMDAN, effort-to-compress complexity, refined composite multiscale dispersion entropy and wavelet threshold denoising. Entropy, 21(1), 11.
- Azami2018Azami, H., & Escudero, J. (2018). Coarse-graining approaches in univariate multiscale sample and dispersion entropy. Entropy, 20(2), 138.