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

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

1 | ```
entropy(x)
``` |

`x` |
a univariate time series |

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.

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

Rob J Hyndman

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.

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