ARMAacf_cpp

Compute the theoretical autocorrelation function for an ARMA process.

internal

Provides a series of tools to compute and plot quantities related to classical and robust wavelet variance for time series and regular lattices. More details can be found, for example, in Serroukh, A., Walden, A.T., & Percival, D.B. (2000) <doi:10.2307/2669537> and Guerrier, S. & Molinari, R. (2016) <arXiv:1607.05858>.

St<c3><a9>phane Guerrier

Wavelet Variance

Stéphane Guerrier

James Balamuta

Justin Lee

Roberto Molinari

Yuming Zhang

Mucyo Karemera

Nathanael Claussen

Haotian Xu

Lionel Voirol

ARMAacf_cpp function

<dl><dt>ar</dt>
<dd>A <code>vector</code> of length p containing AR coefficients</dd>
<dt>ma</dt>
<dd>A <code>vector</code> of length q containing MA coefficients</dd>
<dt>lag_max</dt>
<dd>A <code>unsigned integer</code> indicating the maximum lag necessary</dd></dl>

Arguments

Author

Compute Theoretical ACF for an ARMA Process — ARMAacf_cpp

<dl>

<dt>ar</dt>
<dd>A <code>vector</code> of length p containing AR coefficients</dd>


<dt>ma</dt>
<dd>A <code>vector</code> of length q containing MA coefficients</dd>


<dt>lag_max</dt>
<dd>A <code>unsigned integer</code> indicating the maximum lag necessary</dd>

</dl>

Compute Theoretical ACF for an ARMA Process

ARMAacf_cpp: Compute Theoretical ACF for an ARMA Process

Description

Usage

Value

Arguments

Author

Details