entropy: Spectral entropy of a time series

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/entropy.R

Description

Computes spectral entropy from a univariate normalized spectral density, estimated using an AR model.

Usage

1

Arguments

x

a univariate time series

Details

The spectral entropy equals the Shannon entropy of the spectral density f_x(λ) of a stationary process x_t:

H_s(x_t) = - \int_{-π}^{π} f_x(λ) \log f_x(λ) d λ,

where the density is normalized such that \int_{-π}^{π} f_x(λ) d λ = 1. An estimate of f(λ) can be obtained using spec.ar with the burg method.

Value

A non-negative real value for the spectral entropy H_s(x_t).

Author(s)

Rob J Hyndman

References

Jerry D. Gibson and Jaewoo Jung (2006). “The Interpretation of Spectral Entropy Based Upon Rate Distortion Functions”. IEEE International Symposium on Information Theory, pp. 277-281.

Goerg, G. M. (2013). “Forecastable Component Analysis”. Journal of Machine Learning Research (JMLR) W&CP 28 (2): 64-72, 2013. Available at http://jmlr.org/proceedings/papers/v28/goerg13.html.

See Also

spec.ar

Examples

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tsfeatures documentation built on July 1, 2020, 7:12 p.m.