The ACF function computes the estimated
autocovariance or autocorrelation for both univariate and multivariate cases.

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

ACF function

<dl><dt>x</dt>
<dd>A <code>matrix</code> with dimensions \(N \times S\) or N observations and S processes</dd>
<dt>lagmax</dt>
<dd>A <code>integer</code> indicating the max lag.</dd>
<dt>cor</dt>
<dd>A <code>bool</code> indicating whether the correlation 
(<code>TRUE</code>) or covariance (<code>FALSE</code>) should be computed.</dd>
<dt>demean</dt>
<dd>A <code>bool</code> indicating whether the data should be detrended
(<code>TRUE</code>) or not (<code>FALSE</code>)</dd></dl>

Arguments

Author

Auto-Covariance and Correlation Functions — ACF

<dl>

<dt>x</dt>
<dd>A <code>matrix</code> with dimensions \(N \times S\) or N observations and S processes</dd>


<dt>lagmax</dt>
<dd>A <code>integer</code> indicating the max lag.</dd>


<dt>cor</dt>
<dd>A <code>bool</code> indicating whether the correlation 
(<code>TRUE</code>) or covariance (<code>FALSE</code>) should be computed.</dd>


<dt>demean</dt>
<dd>A <code>bool</code> indicating whether the data should be detrended
(<code>TRUE</code>) or not (<code>FALSE</code>)</dd>

</dl>

Auto-Covariance and Correlation Functions

ACF: Auto-Covariance and Correlation Functions

Description

Usage

Value

Arguments

Author

Details

Examples